Method and apparatus for adjusting read-out conditions and/or image

ABSTRACT

A first image signal representing a radiation image of an object is obtained by exposing a stimulable phosphor sheet, on which the radiation image has been stored, to stimulating rays, which cause the stimulable phosphor sheet to emit light in proportion to the amount of energy stored thereon during its exposure to radiation, the emitted light being detected. A second image signal representing the radiation image is thereafter obtained by again exposing the stimulable phosphor sheet to stimulating rays, the light emitted by the stimulable phosphor sheet being detected. Read-out conditions, under which the second image signal is to be obtained, and/or image processing conditions, under which the second image signal having been obtained is to be image processed, are adjusted on the basis of the first image signal. A storage device stores information representing a standard pattern of radiation images. A signal transforming device transforms the first image signal representing the radiation image into a transformed image signal representing the radiation image, which has been transformed into the standard pattern. A condition adjuster is provided with a neural network, which receives the transformed image signal and feeds out information representing the read-out conditions and/or the image processing conditions.

This is a Continuation of application Ser. No. 07/687,140 filed Apr. 18,1991, now abandoned.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates to a method and apparatus for adjusting read-outconditions and/or image processing conditions for a radiation image,wherein read-out conditions, under which a radiation image is to be readout, and/or image processing conditions, under which an image signalrepresenting the radiation image is to be processed, are adjusted on thebasis of the image signal representing the radiation image. Thisinvention also relates to a radiation image read-out apparatus forreading out a radiation image from a recording medium, such as astimulable phosphor sheet, on which the radiation image of an object hasbeen stored, and an image signal representing the radiation image isthereby obtained. This invention further relates to a radiation imageanalyzing apparatus, wherein characteristic measures representingcharacteristics of a radiation image, such as read-out conditions underwhich the radiation image is to be read out, image processing conditionsunder which the image signal representing the radiation image is to beprocessed, and the portion of an object the image of which was recorded,are found from an image signal representing the radiation image. Thisinvention still further relates to a radiation image analyzing method,wherein a subdivision pattern of radiation images, the shape andlocation of an irradiation field, an orientation in which the object wasplaced when the image of the object was recorded, and/or a portion of anobject the image of which was recorded is determined from an imagesignal representing a radiation image, and a radiation image analyzingapparatus for generating characteristic measures representing theresults of the determination.

2. Description of the Prior Art

Techniques for reading out a recorded radiation image in order to obtainan image signal, carrying out appropriate image processing on the imagesignal, and then reproducing a visible image by use of the processedimage signal have heretofore been known in various fields. For example,as disclosed in Japanese Patent Publication No. 61(1986)-5193, an X-rayimage is recorded on an X-ray film having a small gamma value chosenaccording to the type of image processing to be carried out, the X-rayimage is read out from the X-ray film and converted into an electricsignal (image signal), and the image signal is processed and then usedfor reproducing the X-ray image as a visible image on a copy photograph,or the like. In this manner, a visible image having good image qualitywith high contrast, high sharpness, high graininess, or the like can bereproduced.

Also, when certain kinds of phosphors are exposed to radiation such asX-rays, α-rays, β-rays, γ-rays, cathode rays or ultraviolet rays, theystore part of the energy of the radiation. Then, when the phosphor whichhas been exposed to the radiation is exposed to stimulating rays such asvisible light, light is emitted by the phosphor in proportion to theamount of energy stored thereon during its exposure to the radiation. Aphosphor exhibiting such properties is referred to as a stimulablephosphor.

As disclosed in U.S. Pat. Nos. 4,258,264, 4,276,473, 4,315,318,4,387,428, and Japanese Unexamined Patent Publication No.56(1981)-11395, it has been proposed to use stimulable phosphors inradiation image recording and reproducing systems. Specifically, a sheetprovided with a layer of the stimulable phosphor (hereinafter referredto as a stimulable phosphor sheet) is first exposed to radiation whichhas passed through an object, such as the human body. A radiation imageof the object is thereby stored on the stimulable phosphor sheet. Thestimulable phosphor sheet is then scanned with stimulating rays, such asa laser beam, which cause it to emit light in proportion to the amountof energy stored thereon during its exposure to the radiation. The lightemitted by the stimulable phosphor sheet, upon stimulation thereof, isphotoelectrically detected and converted into an electric image signal.The image signal is then used during the reproduction of the radiationimage of the object as a visible image on a recording material such asphotographic film, on a display device such as a cathode ray tube (CRT)display device, or the like.

Radiation image recording and reproducing systems which use stimulablephosphor sheets are advantageous over conventional radiography usingsilver halide photographic materials, in that images can be recordedeven when the energy intensity of the radiation to which the stimulablephosphor sheet is exposed varies over a wide range. More specifically,since the amount of light which the stimulable phosphor sheet emits whenbeing stimulated varies over a wide range and is proportional to theamount of energy stored thereon during its exposure to the radiation, itis possible to obtain an image having a desirable density regardless ofthe energy intensity of the radiation to which the stimulable phosphorsheet was exposed. In order to obtain the desired image density, anappropriate read-out gain is set when the emitted light is beingdetected and converted into an electric signal to be used in thereproduction of a visible image on a recording material, such asphotographic film, or on a display device, such as a CRT display device.

In order for an image signal to be detected accurately, certain factorswhich affect the image signal must be set in accordance with the dose ofradiation delivered to the stimulable phosphor sheet and the like. Novelradiation image recording and reproducing systems which accuratelydetect an image signal have been proposed. The proposed radiation imagerecording and reproducing systems are constituted such that apreliminary read-out operation (hereinafter simply referred to as the"preliminary readout") is carried out in order approximately toascertain the radiation image stored on the stimulable phosphor sheet.In the preliminary readout, the stimulable phosphor sheet is scannedwith a light beam having a comparatively low energy level, and apreliminary read-out image signal obtained during the preliminaryreadout is analyzed. Thereafter, a final read-out operation (hereinaftersimply referred to as the "final readout") is carried out to obtain theimage signal, which is to be used during the reproduction of a visibleimage. In the final readout, the stimulable phosphor sheet is scannedwith a light beam having an energy level higher than the energy level ofthe light beam used in the preliminary readout, and the radiation imageis read out with the factors affecting the image signal adjusted toappropriate values on the basis of the results of an analysis of thepreliminary read-out image signal.

The term "read-out conditions" as used hereinafter means a group ofvarious factors, which are adjustable and which affect the relationshipbetween the amount of light emitted by the stimulable phosphor sheetduring image readout and the output of a read-out means. For example,the term "read-out conditions" may refer to a read-out gain and a scalefactor which define the relationship between the input to the read-outmeans and the output therefrom, or to the power of the stimulating raysused when the radiation image is read out.

The term "energy level of a light beam" as used herein means the levelof energy of the light beam to which the stimulable phosphor sheet isexposed per unit area. In cases where the energy of the light emitted bythe stimulable phosphor sheet depends on the wavelength of theirradiated light beam, i.e. the sensitivity of the stimulable phosphorsheet to the irradiated light beam depends upon the wavelength of theirradiated light beam, the term "energy level of a light beam" means theweighted energy level which is calculated by weighting the energy levelof the light beam, to which the stimulable phosphor sheet is exposed perunit area, with the sensitivity of the stimulable phosphor sheet to thewavelength. In order to change the energy level of a light beam, lightbeams of different wavelengths may be used, the intensity of the lightbeam produced by a laser beam source or the like may be changed, or theintensity of the light beam may be changed by moving an ND filter or thelike into and out of the optical path of the light beam. Alternatively,the diameter of the light beam may be changed in order to alter thescanning density, or the speed at which the stimulable phosphor sheet isscanned with the light beam may be changed.

Regardless of whether the preliminary readout is or is not carried out,it has also been proposed to analyze the image signal (including thepreliminary read-out image signal) obtained and to adjust the imageprocessing conditions, which are to be used when the image signal isprocessed, on the basis of the results of an analysis of the imagesignal. The term "image processing conditions" as used herein means agroup of various factors, which are adjustable and set when an imagesignal is subjected to processing, which affect the gradation,sensitivity, or the like, of a visible image reproduced from the imagesignal. The proposed method is applicable to cases where an image signalis obtained from a radiation image recorded on a recording medium suchas conventional X-ray film, as well as to systems using stimulablephosphor sheets.

As disclosed in, for example, Japanese Unexamined Patent PublicationNos. 60(1985)-185944 and 61(1986)-280163, operations for calculating thevalues of the read-out conditions for the final readout and/or the imageprocessing conditions are carried out by a group of algorithms whichanalyze an image signal (or a preliminary read-out image signal). Alarge number of image signals detected from a large number of radiationimages are statistically processed. The algorithms which calculate theread-out conditions for the final readout and/or the image processingconditions are designed on the basis of the results obtained from thisprocessing.

In general, the algorithms which have heretofore been employed aredesigned such that a probability density function of an image signal iscreated, and characteristic values are found from the probabilitydensity function. The characteristic values include, for example, themaximum value of the image signal, the minimum value of the imagesignal, or the value of the image signal at which the probabilitydensity function is maximum, i.e. the value which occurs mostfrequently. The read-out conditions for the final readout and/or theimage processing conditions are determined on the basis of thecharacteristic values.

Methods for determining the read-out conditions for the final readoutand/or the image processing conditions on the basis of the results of ananalysis of the probability density function of an image signal can beclassified into the following:

(1) a method as disclosed in Japanese Unexamined Patent Publication No.60(1985)-156055 wherein both the maximum value and the minimum value inthe range resulting in a reproduced visible image containing thenecessary image information are determined from a probability densityfunction of an image signal, and, for example, the read-out conditionsfor the final readout are set such that, during the final readout, theimage information represented by values of the emitted light signalfalling within the range of the maximum value to the minimum value isdetected accurately,

(2) a method as disclosed in Japanese Unexamined Patent Publication No.60(1985)-185944 wherein only the maximum value is determined from aprobability density function of an image signal, a value obtained bysubtracting a predetermined value from the maximum value is taken as theminimum value, and the range between the maximum value and the minimumvalue is taken as the range resulting in a visible image containing thenecessary image information,

(3) a method as disclosed in Japanese Unexamined Patent Publication No.61(1986)-280163 wherein only the minimum value is determined from aprobability density function of an image signal, a value obtained byadding a predetermined value to the minimum value is taken as themaximum value, and the range between the minimum value and the maximumvalue is taken as the range resulting in a visible image containing thenecessary image information,

(4) a method as proposed in Japanese Unexamined Patent Publication No.63(1988)-233658 wherein a difference probability density function isused,

(5) a method as disclosed in Japanese Unexamined Patent Publication No.61(1986)-170730 wherein a cumulative probability density function isused, and

(6) a method as proposed in Japanese Unexamined Patent Publication No.63(1988)-262141 wherein a probability density function is divided into aplurality of small regions by using a discrimination standard. The rangeof an image signal resulting in a visible image containing the necessaryimage information is determined with one of various methods, and theread-out conditions for the final readout and/or the image processingconditions are set with respect to said range.

Recently, a method for utilizing a neural network, which is quitedifferent from the algorithms described above, have been proposed.

Such a neural network is provided with a learning function by backpropagation method. Specifically, when information (an instructorsignal), which represents whether an output signal obtained when aninput signal is given is or is not correct, is fed into the neuralnetwork, the weight of connections between units in the neural network(i.e. the weight of synapse connections) is corrected. By repeating thelearning of the neural network, the probability that a correct answerwill be obtained in response to a new input signal can be kept high.(Such functions are described in, for example, "Learning representationsby back-propagating errors" by D. E. Rumelhart, G. E. Hinton and R. J.Williams, Nature, 323-9,533-356, 1986a; "Back-propagation" by HidekiAso, Computrol, No. 24, pp. 53-60; and "Neural Computer" by KazuyukiAihara, the publishing bureau of Tokyo Denki University).

The neural network is also applicable when the read-out conditions forthe final readout and/or the image processing conditions are to beadjusted. By feeding an image signal, or the like, into the neuralnetwork, outputs representing the values of the read-out conditions forthe final readout and/or the image processing conditions can be obtainedfrom the neural network.

When the neural network is utilized to adjust the read-out conditionsfor the final readout and/or the image processing conditions, byrepeating the learning of the neural network, the read-out conditionsfor the final readout and/or the image processing conditions appropriatefor a specific radiation image can be determined. However, in a singlesystem for processing X-ray images of, for example, the shoulder of ahuman body, various types of image signals are obtained which representvarious radiation images, such as the images of the right shoulder andthe left shoulder (reversed images), an enlarged image and a reducedimage, an erect image and a side image and an inverted image, and imagesshifted from each other. In order for a neural network to be constructedwhich can determine the read-out conditions for the final readout and/orthe image processing conditions appropriate for each of various suchimages, a very large number of units should be incorporated in theneural network. Also, a storage means should be used which has a verylarge capacity for storing information representing the weight ofconnections between units in the neural network. Additionally, thelearning of the neural network should be repeated very many times.

SUMMARY OF THE INVENTION

The primary object of the present invention is to provide an apparatusfor adjusting read-out conditions and/or image processing conditions fora radiation image wherein, even if various image signals representingvarious radiation images are obtained, the read-out conditions for thefinal readout and/or the image processing conditions appropriate foreach of the various radiation images are determined by a neural networkprovided with a comparatively small number of units.

Another object of the present invention is to provide a method foradjusting read-out conditions and/or image processing conditions for aradiation image wherein, even if the image density of a specific regionof interest is apt to vary for different reproduced radiation images dueto, for example, a shift of an object during image recording operations,the read-out conditions for the final readout and/or the imageprocessing conditions are adjusted such that the image density of theregion of interest may be kept at an appropriate level in the reproducedradiation images, and an apparatus for carrying out the method.

A further object of the present invention is to provide a radiationimage read-out apparatus wherein, when radiation images of the sameportion of an object are recorded in the same image recording mode andthe probability density functions of the image signals representing theradiation images are close to each other, the read-out conditions forthe final readout and/or the image processing conditions are adjusted tovalues appropriate for each of the radiation images.

A still further object of the present invention is to provide a methodfor adjusting read-out conditions and/or image processing conditions fora radiation image wherein, when image patterns of an object in aplurality of radiation images shift from each other, the read-outconditions for the final readout and/or the image processing conditionsappropriate for each of the radiation images are determined efficientlyand accurately, and an apparatus for carrying out the method.

Another object of the present invention is to provide a method foradjusting read-out conditions and/or image processing conditions for aradiation image, wherein drawbacks of a method utilizing a probabilitydensity function of an image signal and a method utilizing a neuralnetwork are eliminated, and the read-out conditions for the finalreadout and/or the image processing conditions are adjusted efficientlyand accurately by a neural network, the learning of the neural networkbeing repeated only a few times, and an apparatus for carrying out themethod.

A further object of the present invention is to provide a radiationimage analyzing apparatus, wherein a neural network provided with acomparatively small number of units is utilized, and characteristicmeasures representing characteristics of a radiation image, such asread-out conditions under which the radiation image is to be read out,image processing conditions under which the image signal representingthe radiation image is to be processed, and the portion of an object theimage of which was recorded, are found accurately from an image signalrepresenting the radiation image.

A still further object of the present invention is to provide aradiation image analyzing method, wherein a neural network is utilized,and a subdivision pattern of radiation images, the shape and location ofan irradiation field, an orientation in which the object was placed whenthe image of the object was recorded, and/or a portion of an object theimage of which was recorded is determined accurately from an imagesignal representing a radiation image, and an apparatus for carrying outthe method.

A first apparatus in accordance with the present invention is applicablewhen a stimulable phosphor sheet is used and the preliminary readout iscarried out.

Specifically, the present invention provides, as a first apparatus, anapparatus for adjusting read-out conditions and/or image processingconditions for a radiation image, wherein a first image signalrepresenting a radiation image of an object is obtained by exposing astimulable phosphor sheet, on which the radiation image has been stored,to stimulating rays, which cause the stimulable phosphor sheet to emitlight in proportion to the amount of energy stored thereon during itsexposure to radiation, the emitted light being detected,

a second image signal representing the radiation image is thereafterobtained by again exposing the stimulable phosphor sheet to stimulatingrays, the light emitted by the stimulable phosphor sheet being detected,and

read-out conditions, under which the second image signal is to beobtained, and/or image processing conditions, under which the secondimage signal having been obtained is to be image processed, are adjustedon the basis of the first image signal,

the apparatus for adjusting read-out conditions and/or image processingconditions for a radiation image comprising:

i) a storage means for storing information representing a standardpattern of radiation images,

ii) a signal transforming means for transforming said first image signalrepresenting said radiation image into a transformed image signalrepresenting the radiation image, which has been transformed into saidstandard pattern, and

iii) a condition adjusting means provided with a neural network, whichreceives said transformed image signal and feeds out informationrepresenting the read-out conditions and/or the image processingconditions.

A second apparatus in accordance with the present invention isapplicable when a stimulable phosphor sheet and other recording mediaare used and the image processing conditions are adjusted.

Specifically, the present invention also provides, as a secondapparatus, an apparatus for adjusting image processing conditions for aradiation image, wherein image processing conditions, under which animage signal is to be image processed, are adjusted on the basis of theimage signal representing a radiation image of an object,

the apparatus for adjusting image processing conditions for a radiationimage comprising:

i) a storage means for storing information representing a standardpattern of radiation images,

ii) a signal transforming means for transforming said image signalrepresenting said radiation image into a transformed image signalrepresenting the radiation image, which has been transformed into saidstandard pattern, and

iii) a condition adjusting means provided with a neural network, whichreceives said transformed image signal and feeds out informationrepresenting the image processing conditions.

In the first and second apparatuses in accordance with the presentinvention, no limitation is imposed on what pattern is employed as thestandard pattern of radiation images. The standard pattern may beselected in accordance with the concept behind the design of theapparatuses, or the like.

Also, in the first and second apparatuses in accordance with the presentinvention, no limitation is imposed on how the image signal or the firstimage signal representing the radiation image is transformed into atransformed image signal representing the radiation image, which hasbeen transformed into the standard pattern. For example, the imagesignal or the first image signal representing the radiation image may betransformed into a transformed image signal representing the radiationimage, which has been reversed, rotated, adjusted for the position,enlarged, or reduced.

With the first and second apparatuses in accordance with the presentinvention, the information representing the standard pattern ofradiation images is stored in the storage means. When the image signal(or the first image signal) is obtained, from which the read-outconditions for the final readout and/or the image processing conditionsare to be determined, the image signal representing the radiation imageis transformed into a transformed image signal representing theradiation image, which has been transformed into the standard pattern.Thereafter, the transformed image signal is fed into the neural network.Therefore, a neural network having a small scale may be used, and astorage means having a small storage capacity may be used to store theweight of connections between units of the neural network. Also, whenthe level of accuracy, with which the read-out conditions for the finalreadout and/or the image processing conditions are to be determined, iskept the same, the learning of the neural network may be repeated only afewer times than when a conventional technique is used.

A first method in accordance with the present invention is applicablewhen a stimulable phosphor sheet is used and the preliminary readout iscarried out.

Specifically, the present invention further provides, as a first method,a method for adjusting read-out conditions and/or image processingconditions for a radiation image, wherein a first image signalrepresenting a radiation image of an object is obtained by exposing astimulable phosphor sheet, on which the radiation image has been stored,to stimulating rays, which cause the stimulable phosphor sheet to emitlight in proportion to the amount of energy stored thereon during itsexposure to radiation, the emitted light being detected,

a second image signal representing the radiation image is thereafterobtained by again exposing the stimulable phosphor sheet to stimulatingrays, the light emitted by the stimulable phosphor sheet being detected,and

read-out conditions, under which the second image signal is to beobtained, and/or image processing conditions, under which the secondimage signal having been obtained is to be image processed, are adjustedon the basis of the first image signal,

the method for adjusting read-out conditions and/or image processingconditions for a radiation image comprising the steps of:

i) carrying out a condition adjustment by using a neural network, whichreceives said first image signal and feeds out information representingthe read-out conditions and/or the image processing conditions, and

ii) when learning of said neural network is carried out such thatinformation representing appropriate read-out conditions and/orappropriate image processing conditions may be fed out, utilizing animage signal representing a radiation image, in which a pattern of aspecific region of interest in an object is embedded, and read-outconditions and/or image processing conditions, which have beendetermined as being optimum for the pattern of said region of interest.

A second method in accordance with the present invention is applicablewhen a stimulable phosphor sheet is used and the preliminary readout iscarried out.

Specifically, the present invention still further provides, as a secondmethod, a method for adjusting read-out conditions and/or imageprocessing conditions for a radiation image, wherein a first imagesignal representing a radiation image of an object is obtained byexposing a stimulable phosphor sheet, on which the radiation image hasbeen stored, to stimulating rays, which cause the stimulable phosphorsheet to emit light in proportion to the amount of energy stored thereonduring its exposure to radiation, the emitted light being detected,

a second image signal representing the radiation image is thereafterobtained by again exposing the stimulable phosphor sheet to stimulatingrays, the light emitted by the stimulable phosphor sheet being detected,and

read-out conditions, under which the second image signal is to beobtained, and/or image processing conditions, under which the secondimage signal having been obtained is to be image processed, are adjustedon the basis of the first image signal,

the method for adjusting read-out conditions and/or image processingconditions for a radiation image comprising the steps of:

i) extracting the image signal components of said first image signal,which represent a pattern of a specific region of interest in saidobject, said pattern being embedded in said radiation image, by using aneural network, which receives said first image signal made up of aseries of image signal components and feeds out information representingthe shape and location of the pattern of said region of interest, and

ii) adjusting the read-out conditions and/or the image processingconditions on the basis of the extracted image signal components of saidfirst image signal.

A third method in accordance with the present invention is applicablewhen a stimulable phosphor sheet and other recording media are used andthe image processing conditions are adjusted.

Specifically, the present invention also provides, as a third method, amethod for adjusting image processing conditions for a radiation image,wherein image processing conditions, under which an image signal is tobe image processed, are adjusted on the basis of the image signalrepresenting a radiation image of an object,

the method for adjusting image processing conditions for a radiationimage comprising the steps of:

i) carrying out a condition adjustment by using a neural network, whichreceives said image signal and feeds out information representing theimage processing conditions, and

ii) when learning of said neural network is carried out such thatinformation representing appropriate image processing conditions may befed out, utilizing an image signal representing a radiation image, inwhich a pattern of a specific region of interest in an object isembedded, and image processing conditions, which have been determined asbeing optimum for the pattern of said region of interest.

A fourth method in accordance with the present invention is applicablewhen a stimulable phosphor sheet and other recording media are used andthe image processing conditions are adjusted.

Specifically, the present invention further provides, as a fourthmethod, a method for adjusting image processing conditions for aradiation image, wherein image processing conditions, under which animage signal is to be image processed, are adjusted on the basis of theimage signal representing a radiation image of an object,

the method for adjusting image processing conditions for a radiationimage comprising the steps of:

i) extracting the image signal components of said image signal, whichrepresent a pattern of a specific region of interest in said object,said pattern being embedded in said radiation image, by using a neuralnetwork, which receives said image signal made up of a series of imagesignal components and feeds out information representing the shape andlocation of the pattern of said region of interest, and

ii) adjusting the image processing conditions on the basis of theextracted image signal components of said image signal.

Conventional methods for adjusting the read-out conditions for the finalreadout and/or the image processing conditions on the basis of theresults of an analysis of a probability density function of an imagesignal can work efficiently in many cases. However, with theconventional methods, problems often occur depending on how a radiationimage of an object is recorded. The reasons why the problems occur willbe described hereinbelow by taking radiation images, in which patternsof a shoulder joint are embedded, as an example.

FIGS. 6A and 6B show radiation images in which a pattern of a shoulderjoint 9 is embedded. The two radiation images differ from each other inthat the radiation image of FIG. 6B includes the patterns of vertebralbodies 10, and the radiation image of FIG. 6A does not include them.FIGS. 7A and 7B show probability density functions of image signalsrepresenting the radiation images shown in FIGS. 6A and 6B.

As shown in FIGS. 7A and 7B, the two probability density functions areapproximately identical with each other. However, the two radiationimages have the difference described above. Therefore, the image signalcomponents representing the pattern of the shoulder joint 9, which istaken as a region of interest, fall within the range K1 in theprobability density function shown in FIG. 7A and within the range K2 inthe probability density function shown in FIG. 7B. When the read-outconditions for the final readout and/or the image processing conditionsare determined from each of the two probability density functions, andvisible images of the radiation images shown in FIGS. 6A and 6B arereproduced from the image signals obtained under these conditions,because the two probability density functions are approximatelyidentical with each other, approximately the same values are calculatedas the read-out conditions for the final readout and/or the imageprocessing conditions. As a result, two reproduced visible images areobtained which have approximately the same image density and contrast.Therefore, the image density of the pattern of the shoulder joint, whichis taken as the region of interest, cannot be kept appropriate.

In such cases, the pattern of the region of interest is not illustratedclearly in the reproduced visible image. Also, for example, when aplurality of reproduced visible images are compared with each other inorder for the course of an abnormal part of an object to beinvestigated, a correct diagnosis cannot be made.

The first, second, third, and fourth methods in accordance with thepresent invention eliminates the problems described above.

With the first and third methods in accordance with the presentinvention, the neural network is used to determine the read-outconditions for the final readout and/or the image processing conditions.By repeating the learning of the neural network, appropriate read-outconditions for the final readout and/or appropriate image processingconditions can be determined.

Also, the learning of the neural network is carried out by utilizing animage signal representing a radiation image, in which a pattern of aspecific region of interest in an object is embedded, and the read-outconditions for the final readout and/or the image processing conditions,which have been determined as being optimum for the pattern of theregion of interest. Therefore, even if the radiation image includes thepattern of the region of interest and other patterns, the read-outconditions for the final readout and/or the image processing conditionscan be set to values appropriate for the pattern of the region ofinterest.

With the second and fourth methods in accordance with the presentinvention, by repeating the learning of the neural network, the shapeand location of the pattern of the region of interest can be determinedaccurately. The read-out conditions for the final readout and/or theimage processing conditions are then determined on the basis of theimage signal components of the first image signal representing thepattern of the region of interest. Therefore, the read-out conditionsfor the final readout and/or the image processing conditions arebasically free of adverse effects of the image information other thanthe pattern of the region of interest and can be set to be appropriatefor the pattern of the region of interest.

In order for the read-out conditions for the final readout and/or theimage processing conditions to be adjusted on the basis of the imagesignal components of the first image signal representing the pattern ofthe region of interest, a technique for analyzing a probability densityfunction may be utilized. Alternatively, a neural network independent ofthe neural network for determining the shape and location of the patternof the region of interest may be utilized.

A fifth method in accordance with the present invention is applicablewhen a stimulable phosphor sheet is used and the preliminary readout iscarried out.

Specifically, the present invention still further provides, as a fifthmethod, a method for adjusting read-out conditions and/or imageprocessing conditions for a radiation image, wherein a first imagesignal representing a radiation image of an object is obtained byexposing a stimulable phosphor sheet, on which the radiation image hasbeen stored, to stimulating rays, which cause the stimulable phosphorsheet to emit light in proportion to the amount of energy stored thereonduring its exposure to radiation, the emitted light being detected,

a second image signal representing the radiation image is thereafterobtained by again exposing the stimulable phosphor sheet to stimulatingrays, the light emitted by the stimulable phosphor sheet being detected,and

read-out conditions, under which the second image signal is to beobtained, and/or image processing conditions, under which the secondimage signal having been obtained is to be image processed, are adjustedon the basis of the first image signal,

the method for adjusting read-out conditions and/or image processingconditions for a radiation image comprising the steps of:

i) carrying out a temporary condition adjustment by using a probabilitydensity function analyzing means, which receives said first imagesignal, temporarily adjusts the read-out conditions and/or the imageprocessing conditions on the basis of the results of an analysis of aprobability density function of said first image signal, and feeds outinformation representing the read-out conditions and/or the imageprocessing conditions, which have been adjusted temporarily,

ii) correcting the read-out conditions and/or the image processingconditions, which have been adjusted temporarily by said probabilitydensity function analyzing means, by using a neural network, whichreceives said first image signal and feeds out information representingcorrection values to be used in correcting the read-out conditionsand/or the image processing conditions, which have been adjustedtemporarily, and

iii) thereby finally adjusting the read-out conditions and/or the imageprocessing conditions.

A sixth method in accordance with the present invention is applicablewhen image signals detected from radiation images stored on a stimulablephosphor sheet and other recording media are processed.

Specifically, the present invention also provides, as a sixth method, amethod for adjusting image processing conditions for a radiation image,wherein image processing conditions, under which an image signal is tobe image processed, are adjusted on the basis of the image signalrepresenting a radiation image of an object,

the method for adjusting image processing conditions for a radiationimage comprising the steps of:

i) carrying out a temporary condition adjustment by using a probabilitydensity function analyzing means, which receives said image signal,temporarily adjusts the image processing conditions on the basis of theresults of an analysis of a probability density function of said imagesignal, and feeds out information representing the image processingconditions, which have been adjusted temporarily,

ii) correcting the image processing conditions, which have been adjustedtemporarily by said probability density function analyzing means, byusing a neural network, which receives said image signal and feeds outinformation representing correction values to be used in correcting theimage processing conditions, which have been adjusted temporarily, and

iii) thereby finally adjusting the image processing conditions.

The fifth and sixth methods in accordance with the present invention arecharacterized by carrying out necessary corrections by using the neuralnetwork in a method for adjusting read-out conditions and/or imageprocessing conditions for a radiation image wherein there is the riskthat errors may occur in adjusting the conditions when only the analysisof a probability density function is carried out.

A seventh method in accordance with the present invention is applicablewhen a stimulable phosphor sheet is used and the preliminary readout iscarried out.

Specifically, the present invention further provides, as a seventhmethod, a method for adjusting read-out conditions and/or imageprocessing conditions for a radiation image, wherein a first imagesignal representing a radiation image of an object is obtained byexposing a stimulable phosphor sheet, on which the radiation image hasbeen stored, to stimulating rays, which cause the stimulable phosphorsheet to emit light in proportion to the amount of energy stored thereonduring its exposure to radiation, the emitted light being detected,

a second image signal representing the radiation image is thereafterobtained by again exposing the stimulable phosphor sheet to stimulatingrays, the light emitted by the stimulable phosphor sheet being detected,and

read-out conditions, under which the second image signal is to beobtained, and/or image processing conditions, under which the secondimage signal having been obtained is to be image processed, are adjustedon the basis of the first image signal,

the method for adjusting read-out conditions and/or image processingconditions for a radiation image comprising the steps of:

i) carrying out a temporary condition adjustment by using a probabilitydensity function analyzing means, which receives said first imagesignal, temporarily adjusts the read-out conditions and/or the imageprocessing conditions on the basis of the results of an analysis of aprobability density function of said first image signal, and feeds outinformation representing the read-out conditions and/or the imageprocessing conditions, which have been adjusted temporarily, and

ii) finally adjusting the read-out conditions and/or the imageprocessing conditions by using a neural network, which receives saidfirst image signal and said information representing the read-outconditions and/or the image processing conditions having been adjustedtemporarily and feeds out information representing the read-outconditions and/or the image processing conditions, which have beenadjusted finally.

An eighth method in accordance with the present invention is applicablewhen image signals detected from radiation images stored on a stimulablephosphor sheet and other recording media are processed.

Specifically, the present invention also provides, as an eighth method,a method for adjusting image processing conditions for a radiationimage, wherein image processing conditions, under which an image signalis to be image processed, are adjusted on the basis of the image signalrepresenting a radiation image of an object,

the method for adjusting image processing conditions for a radiationimage comprising the steps of:

i) carrying out a temporary condition adjustment by using a probabilitydensity function analyzing means, which receives said image signal,temporarily adjusts the image processing conditions on the basis of theresults of an analysis of a probability density function of said imagesignal, and feeds out information representing the image processingconditions, which have been adjusted temporarily, and

ii) finally adjusting the image processing conditions by using a neuralnetwork, which receives said image signal and said informationrepresenting the image processing conditions having been adjustedtemporarily and feeds out information representing the image processingconditions, which have been adjusted finally.

The seventh and eighth methods in accordance with the present inventionare characterized by employing the read-out conditions for the finalreadout and/or the image processing conditions, which have beendetermined on the basis of the results of an analysis of the probabilitydensity function of the image signal, as temporary adjusted conditionsin a method for adjusting read-out conditions and/or image processingconditions for a radiation image wherein there is the risk that errorsmay occur in adjusting the conditions when only the analysis of aprobability density function is carried out. The read-out conditions forthe final readout and/or the image processing conditions are thenfinally adjusted by the neural network on the basis of the image signaland the temporary adjusted conditions.

The present invention further provides a third apparatus for carryingout the fifth method in accordance with the present invention.

Specifically, the present invention further provides, as a thirdapparatus, an apparatus for adjusting read-out conditions and/or imageprocessing conditions for a radiation image, wherein a first imagesignal representing a radiation image of an object is obtained byexposing a stimulable phosphor sheet, on which the radiation image hasbeen stored, to stimulating rays, which cause the stimulable phosphorsheet to emit light in proportion to the amount of energy stored thereonduring its exposure to radiation, the emitted light being detected,

a second image signal representing the radiation image is thereafterobtained by again exposing the stimulable phosphor sheet to stimulatingrays, the light emitted by the stimulable phosphor sheet being detected,and

read-out conditions, under which the second image signal is to beobtained, and/or image processing conditions, under which the secondimage signal having been obtained is to be image processed, are adjustedon the basis of the first image signal,

the apparatus for adjusting read-out conditions and/or image processingconditions for a radiation image comprising:

i) a probability density function analyzing means, which receives saidfirst image signal, temporarily adjusts the read-out conditions and/orthe image processing conditions on the basis of the results of ananalysis of a probability density function of said first image signal,and feeds out information representing the read-out conditions and/orthe image processing conditions, which have been adjusted temporarily,

ii) a neural network, which receives said first image signal and feedsout information representing correction values to be used in correctingthe read-out conditions and/or the image processing conditions, whichhave been adjusted temporarily by said probability density functionanalyzing means, and

iii) an addition means for adding said correction values, which arerepresented by the information received from said neural network, to theread-out conditions and/or the image processing conditions, which havebeen adjusted temporarily by said probability density function analyzingmeans, and feeding out information representing the read-out conditionsand/or the image processing conditions, which have thus been adjustedfinally.

The present invention still further provides, as a fourth apparatus, anapparatus for adjusting image processing conditions for a radiationimage, wherein image processing conditions, under which an image signalis to be image processed, are adjusted on the basis of the image signalrepresenting a radiation image of an object,

the apparatus for adjusting image processing conditions for a radiationimage comprising:

i) a probability density function analyzing means, which receives saidimage signal, temporarily adjusts the image processing conditions on thebasis of the results of an analysis of a probability density function ofsaid image signal, and feeds out information representing the imageprocessing conditions, which have been adjusted temporarily,

ii) a neural network, which receives said image signal and feeds outinformation representing correction values to be used in correcting theimage processing conditions, which have been adjusted temporarily bysaid probability density function analyzing means, and

iii) an addition means for adding said correction values, which arerepresented by the information received from said neural network, to theimage processing conditions, which have been adjusted temporarily bysaid probability density function analyzing means, and feeding outinformation representing the image processing conditions, which havethus been adjusted finally.

The present invention also provides a fifth apparatus for carrying outthe seventh method in accordance with the present invention.

Specifically, the present invention also provides, as a fifth apparatus,an apparatus for adjusting read-out conditions and/or image processingconditions for a radiation image, wherein a first image signalrepresenting a radiation image of an object is obtained by exposing astimulable phosphor sheet, on which the radiation image has been stored,to stimulating rays, which cause the stimulable phosphor sheet to emitlight in proportion to the amount of energy stored thereon during itsexposure to radiation, the emitted light being detected,

a second image signal representing the radiation image is thereafterobtained by again exposing the stimulable phosphor sheet to stimulatingrays, the light emitted by the stimulable phosphor sheet being detected,and

read-out conditions, under which the second image signal is to beobtained, and/or image processing conditions, under which the secondimage signal having been obtained is to be image processed, are adjustedon the basis of the first image signal,

the apparatus for adjusting read-out conditions and/or image processingconditions for a radiation image comprising:

i) a probability density function analyzing means, which receives saidfirst image signal, temporarily adjusts the read-out conditions and/orthe image processing conditions on the basis of the results of ananalysis of a probability density function of said first image signal,and feeds out information representing the read-out conditions and/orthe image processing conditions, which have been adjusted temporarily,and

ii) a neural network, which receives said first image signal and saidinformation representing the read-out conditions and/or the imageprocessing conditions having been adjusted temporarily and feeds outinformation representing the read-out conditions and/or the imageprocessing conditions, which have been adjusted finally.

The present invention further provides, as a sixth apparatus, anapparatus for adjusting image processing conditions for a radiationimage, wherein image processing conditions, under which an image signalis to be image processed, are adjusted on the basis of the image signalrepresenting a radiation image of an object,

the apparatus for adjusting image processing conditions for a radiationimage comprising:

i) a probability density function analyzing means, which receives saidimage signal, temporarily adjusts the image processing conditions on thebasis of the results of an analysis of a probability density function ofsaid image signal, and feeds out information representing the imageprocessing conditions, which have been adjusted temporarily, and

ii) a neural network, which receives said image signal and saidinformation representing the image processing conditions having beenadjusted temporarily and feeds out information representing the imageprocessing conditions, which have been adjusted finally.

By feeding an image signal representing a radiation image into a neuralnetwork, the read-out conditions for the final readout and/or the imageprocessing conditions can be adjusted only with the neural network.However, in the fifth and sixth methods and the third and fourthapparatuses in accordance with the present invention, a neural networkis used, which receives the image signal and feeds out informationrepresenting correction values to be used in correcting the read-outconditions for the final readout and/or the image processing conditions,which have been adjusted temporarily by the probability density functionanalyzing means. The read-out conditions for the final readout and/orthe image processing conditions, which have been adjusted temporarily bythe probability density function analyzing means, are corrected with thecorrection values. The read-out conditions for the final readout and/orthe image processing conditions are thereby adjusted finally.

In the seventh and eighth methods and the fifth and sixth apparatuses inaccordance with the present invention, the image signal representing aradiation image and the information representing the read-out conditionsfor the final readout and/or the image processing conditions, which havebeen adjusted temporarily, are fed into the neural network. From theneural network, the information representing the read-out conditions forthe final readout and/or the image processing conditions, which havebeen adjusted finally, are fed out.

As described above, in the fifth and sixth methods and the third andfourth apparatuses in accordance with the present invention, the neuralnetwork is used, which receives the image signal and feeds out theinformation representing the necessary correction values. In the seventhand eighth methods and the fifth and sixth apparatuses in accordancewith the present invention, the neural network is used, which receivesthe image signal and the information representing the read-outconditions for the final readout and/or the image processing conditionshaving been adjusted temporarily and feeds out the informationrepresenting the read-out conditions for the final readout and/or theimage processing conditions, which have been adjusted finally. When thelearning of the neural network is carried out, the informationrepresenting the necessary correction values or the informationrepresenting the final read-out conditions for the final readout and/orthe final image processing conditions are used as instructor signals.

With the fifth and sixth methods and the third and fourth apparatuses inaccordance with the present invention, the read-out conditions for thefinal readout and/or the image processing conditions, which have beenadjusted temporarily by the probability density function analyzingmeans, are corrected by the neural network, which receives the imagesignal and feeds out the information representing correction values tobe used in correcting the read-out conditions for the final readoutand/or the image processing conditions, which have been adjustedtemporarily. The read-out conditions for the final readout and/or theimage processing conditions are thereby adjusted finally. Also, with theseventh and eighth methods and the fifth and sixth apparatuses inaccordance with the present invention, the image signal and theinformation representing the read-out conditions for the final readoutand/or the image processing conditions, which have been adjustedtemporarily by the probability density function analyzing means, are fedinto the neural network. The information representing the read-outconditions for the final readout and/or the image processing conditions,which have been adjusted finally, are fed out from the neural network.Therefore, errors occurring when only the analysis of a probabilitydensity function of an image signal is carried out can be eliminated,and the read-out conditions for the final readout and/or the imageprocessing conditions can be adjusted accurately.

The present invention still further provides, as a seventh apparatus, aradiation image read-out apparatus wherein a stimulable phosphor sheetis used and the preliminary readout is carried out.

Specifically, the present invention still further provides, as a seventhapparatus, a radiation image read-out apparatus comprising:

i) a preliminary read-out means for exposing a stimulable phosphorsheet, on which the radiation image has been stored, to stimulatingrays, which cause the stimulable phosphor sheet to emit light inproportion to the amount of energy stored thereon during its exposure toradiation, detecting the emitted, and thereby obtaining a preliminaryread-out image signal representing said radiation image of said object,

ii) a final read-out means for again exposing said stimulable phosphorsheet to stimulating rays, which cause said stimulable phosphor sheet toemit light in proportion to the amount of energy stored thereon duringits exposure to radiation, detecting the emitted, and thereby obtaininga final read-out image signal representing said radiation image of saidobject,

iii) a latitude operating means for creating a probability densityfunction of said preliminary read-out image signal, and determining alatitude on the basis of the results of an analysis of the probabilitydensity function, the latitude constituting one of read-out conditions,under which said final read-out image signal is to be obtained, and/orimage processing conditions, under which said final read-out imagesignal having been obtained is to be image processed, and

iv) a sensitivity operating means provided with a neural network, whichreceives said preliminary read-out image signal and feeds outinformation representing sensitivity, the sensitivity constituting oneof the read-out conditions and/or the image processing conditions.

The present invention also provides, as an eighth apparatus, a radiationimage read-out apparatus wherein a stimulable phosphor sheet and otherrecording media are used and no preliminary readout is carried out.

Specifically, the present invention also provides, as an eighthapparatus, a radiation image read-out apparatus comprising:

i) a read-out means for reading out a radiation image of an object froma recording media, on which the radiation image has been recorded, andthereby obtaining an image signal representing said radiation image ofsaid object,

ii) a latitude operating means for creating a probability densityfunction of said image signal, and determining a latitude on the basisof the results of an analysis of the probability density function, thelatitude constituting one of image processing conditions, under whichsaid image signal is to be image processed, and

iii) a sensitivity operating means provided with a neural network, whichreceives said image signal and feeds out information representingsensitivity, the sensitivity constituting one of the image processingconditions.

The read-out conditions for the final readout can be classified into thelatitude Gp and the sensitivity Sk. The latitude Gp corresponds to theratio of the largest amount of emitted light, which is capable of beingaccurately converted into an image signal, to the smallest amount ofemitted light, which is capable of being accurately converted into animage signal. The sensitivity Sk corresponds to the photoelectricconversion factor, which represents to what image signal level apredetermined amount of emitted light is to be converted.

The image processing conditions can be classified into the latitude Gpand the sensitivity Sk. In this case, the latitude Gp corresponds to themagnification of conversion of the span of an image signal, whichrepresents what span of the original image signal is to be convertedinto the whole span (e.g. four orders of ten) of the processed imagesignal. Also, the sensitivity Sk corresponds to the amount of shift ofthe value of an image signal, which represents to what image signalvalue the original image signal having a predetermined value is to beconverted.

In cases where a plurality of images of the same portion of an objectwere recorded in the same image recording mode and the region ofinterest is the same, even if the object shifted during a plurality ofoperations for recording the images, the latitude Gp is kept the same,and only the sensitivity Sk varies for different images.

Therefore, in the seventh and eighth apparatuses in accordance with thepresent invention, the latitude Gp is determined on the basis of theresults of an analysis of the probability density function of the imagesignal (or the preliminary read-out image signal). The sensitivity Sk isdirectly determined from the radiation image by using the neuralnetwork.

The learning of the neural network is carried out such that, when theimage signal (or the preliminary read-out image signal) is fed into theneural network, it may feed out the information representing anappropriate sensitivity Sk. Therefore, for example, in cases where theobject shifted during a plurality of operations for recording the imagesand the image patterns of the object in a plurality of radiation imagesshift from each other, the sift can be found and an appropriatesensitivity Sk can be determined.

As described above, the latitude Gp is determined on the basis of theresults of an analysis of the probability density function of the imagesignal (or the preliminary read-out image signal), and the sensitivitySk is determined by the neural network. Therefore, even if a shift of anobject during the image recording operations, or the like, occurred andappropriate read-out conditions for the final readout and/or appropriateimage processing conditions cannot be determined only with aconventional analysis of the probability density function, the read-outconditions for the final readout and/or the image processing conditionscan be adjusted accurately with the seventh and eighth apparatuses inaccordance with the present invention.

The neural network may be constructed such that it can determine boththe sensitivity Sk and the latitude Gp.

However, in cases where the neural network may be constructed such thatit can determine both the sensitivity Sk and the latitude Gp, the neuralnetwork becomes very complicated, and a storage means having a verylarge storage capacity should be used to store information representingthe weight of connections between units, which constitute the neuralnetwork. Also, the learning of the neural network should be repeatedvery many times. Additionally, a long time is required between when theimage signal (or the preliminary read-out image signal) is fed into theneural network and when the sensitivity Sk and the latitude Gp aredetermined.

The seventh and eighth apparatuses in accordance with the presentinvention are provided with both the latitude operating means foranalyzing the probability density function and the neural network.However, the seventh and eighth apparatuses in accordance with thepresent invention can be kept simpler in configuration than an apparatuswherein both the sensitivity Sk and the latitude Gp are determined by aneural network. Also, with the seventh and eighth apparatuses inaccordance with the present invention, a storage means having a smallstorage capacity may be used, and the sensitivity Sk and the latitude Gpcan be adjusted quickly. Additionally, the learning of the neuralnetwork may be repeated only a few times.

A ninth method in accordance with the present invention is applicablewhen a stimulable phosphor sheet is used and the preliminary readout iscarried out.

Specifically, the present invention further provides, as a ninth method,a method for adjusting read-out conditions and/or image processingconditions for a radiation image, wherein a first image signalrepresenting a radiation image of an object is obtained by exposing astimulable phosphor sheet, on which the radiation image has been stored,to stimulating rays, which cause the stimulable phosphor sheet to emitlight in proportion to the amount of energy stored thereon during itsexposure to radiation, the emitted light being detected,

a second image signal representing the radiation image is thereafterobtained by again exposing the stimulable phosphor sheet to stimulatingrays, the light emitted by the stimulable phosphor sheet being detected,and

read-out conditions, under which the second image signal is to beobtained, and/or image processing conditions, under which the secondimage signal having been obtained is to be image processed, are adjustedon the basis of the first image signal,

the method for adjusting read-out conditions and/or image processingconditions for a radiation image comprising the steps of:

i) using a neural network, which receives said first image signal andfeeds out information representing the read-out conditions and/or theimage processing conditions, and

ii) feeding information, which represents the position of the centerpoint of the pattern of said object in said radiation image, into saidneural network,

said neural network adjusting the read-out conditions and/or the imageprocessing conditions on the basis of said first image signal by takingthe position of the center point of the pattern of said object intoconsideration.

The ninth method is carried out by a ninth apparatus in accordance withthe present invention.

Specifically, the present invention still further provides, as a ninthapparatus, an apparatus for adjusting read-out conditions and/or imageprocessing conditions for a radiation image, wherein a first imagesignal representing a radiation image of an object is obtained byexposing a stimulable phosphor sheet, on which the radiation image hasbeen stored, to stimulating rays, which cause the stimulable phosphorsheet to emit light in proportion to the amount of energy stored thereonduring its exposure to radiation, the emitted light being detected,

a second image signal representing the radiation image is thereafterobtained by again exposing the stimulable phosphor sheet to stimulatingrays, the light emitted by the stimulable phosphor sheet being detected,and

read-out conditions, under which the second image signal is to beobtained, and/or image processing conditions, under which the secondimage signal having been obtained is to be image processed, are adjustedon the basis of the first image signal,

the apparatus for adjusting read-out conditions and/or image processingconditions for a radiation image comprising:

i) a means for determining the position of the center point of thepattern of said object in said radiation image from said first imagesignal, and feeding out information representing the position of thecenter point of the pattern of said object, and

ii) a neural network, which receives said first image signal and theoutput of said means for determining the position of the center point ofthe pattern of said object, adjusts the read-out conditions and/or theimage processing conditions on the basis of said first image signal bytaking the position of the center point of the pattern of said objectinto consideration, and feeds out information representing theconditions, which have thus been adjusted.

A tenth method in accordance with the present invention is applicablewhen a stimulable phosphor sheet and other recording media are used andthe image processing conditions are adjusted.

Specifically, the present invention also provides, as a tenth method, amethod for adjusting image processing conditions for a radiation image,wherein image processing conditions, under which an image signal is tobe image processed, are adjusted on the basis of the image signalrepresenting a radiation image of an object,

the method for adjusting image processing conditions for a radiationimage comprising the steps of:

i) using a neural network, which receives said image signal and feedsout information representing the image processing conditions, and

ii) feeding information, which represents the position of the centerpoint of the pattern of said object in said radiation image, into saidneural network,

said neural network adjusting the image processing conditions on thebasis of said image signal by taking the position of the center point ofthe pattern of said object into consideration.

The tenth method is carried out by a tenth apparatus in accordance withthe present invention.

Specifically, the present invention further provides, as a tenthapparatus, an apparatus for adjusting image processing conditions for aradiation image, wherein image processing conditions, under which animage signal is to be image processed, are adjusted on the basis of theimage signal representing a radiation image of an object,

the apparatus for adjusting image processing conditions for a radiationimage comprising:

i) a means for determining the position of the center point of thepattern of said object in said radiation image from said image signal,and feeding out information representing the position of the centerpoint of the pattern of said object, and

ii) a neural network, which receives said image signal and the output ofsaid means for determining the position of the center point of thepattern of said object, adjusts the image processing conditions on thebasis of said image signal by taking the position of the center point ofthe pattern of said object into consideration, and feeds out informationrepresenting the conditions, which have thus been adjusted.

With the ninth and tenth methods in accordance with the presentinvention, a neural network is used, which receives the image signal andfeeds out information representing the read-out conditions for the finalreadout and/or the image processing conditions. Information, whichrepresents the position of the center point of the pattern of the objectin the radiation image, is fed into the neural network. The neuralnetwork adjusts the read-out conditions for the final readout and/or theimage processing conditions on the basis of the image signal by takingthe position of the center point of the pattern of the object intoconsideration. Therefore, even if the pattern of the object in theradiation image shifts, the neural network can accurately finds theposition of the pattern of the object in the radiation image and carryout operations for judgment with respect to the image signal close to animage signal representing a standard image. Accordingly, the read-outconditions for the final readout and/or the image processing conditionscan be adjusted accurately and efficiently.

In order for the position of the center point of the pattern of theobject in the radiation image to be determined, the method disclosed in,for example, Japanese Unexamined Patent Publication No. 2(1990)-28782may be employed.

An eleventh method in accordance with the present invention isapplicable when a stimulable phosphor sheet is used and the preliminaryreadout is carried out.

Specifically, the present invention still further provides, as aneleventh method, a method for adjusting read-out conditions and/or imageprocessing conditions for a radiation image, wherein a first imagesignal representing a radiation image of an object is obtained byexposing a stimulable phosphor sheet, on which the radiation image hasbeen stored, to stimulating rays, which cause the stimulable phosphorsheet to emit light in proportion to the amount of energy stored thereonduring its exposure to radiation, the emitted light being detected,

a second image signal representing the radiation image is thereafterobtained by again exposing the stimulable phosphor sheet to stimulatingrays, the light emitted by the stimulable phosphor sheet being detected,and

read-out conditions, under which the second image signal is to beobtained, and/or image processing conditions, under which the secondimage signal having been obtained is to be image processed, are adjustedon the basis of the first image signal,

the method for adjusting read-out conditions and/or image processingconditions for a radiation image comprising the steps of:

i) feeding information, which represents a probability density functionof said first image signal, into a neural network, and

ii) feeding out information representing the read-out conditions and/orthe image processing conditions from said neural network.

The present invention also provides, as a twelfth method, a method foradjusting read-out conditions and/or image processing conditions for aradiation image, wherein a first image signal representing a radiationimage of an object is obtained by exposing a stimulable phosphor sheet,on which the radiation image has been stored, to stimulating rays, whichcause the stimulable phosphor sheet to emit light in proportion to theamount of energy stored thereon during its exposure to radiation, theemitted light being detected,

a second image signal representing the radiation image is thereafterobtained by again exposing the stimulable phosphor sheet to stimulatingrays, the light emitted by the stimulable phosphor sheet being detected,and

read-out conditions, under which the second image signal is to beobtained, and/or image processing conditions, under which the secondimage signal having been obtained is to be image processed, are adjustedon the basis of the first image signal,

the method for adjusting read-out conditions and/or image processingconditions for a radiation image comprising the steps of:

i) feeding information, which represents a probability density functionof said first image signal, and subsidiary information, which givesspecifics about said radiation image stored on said stimulable phosphorsheet, into a neural network, and

ii) feeding out information representing the read-out conditions and/orthe image processing conditions from said neural network.

The present invention further provides, as a thirteenth method, a methodfor adjusting read-out conditions and/or image processing conditions fora radiation image, wherein a first image signal representing a radiationimage of an object is obtained by exposing a stimulable phosphor sheet,on which the radiation image has been stored, to stimulating rays, whichcause the stimulable phosphor sheet to emit light in proportion to theamount of energy stored thereon during its exposure to radiation, theemitted light being detected,

a second image signal representing the radiation image is thereafterobtained by again exposing the stimulable phosphor sheet to stimulatingrays, the light emitted by the stimulable phosphor sheet being detected,and

read-out conditions, under which the second image signal is to beobtained, and/or image processing conditions, under which the secondimage signal having been obtained is to be image processed, are adjustedon the basis of the first image signal,

the method for adjusting read-out conditions and/or image processingconditions for a radiation image comprising the steps of:

i) taking the value of said first image signal, which value representsthe maximum amount of the emitted light in part of a probability densityfunction of said first image signal other than the part corresponding toa background region in said radiation image, as the maximum value,

ii) normalizing said probability density function with its maximum valuein its part between said maximum value and the minimum value of saidfirst image signal, a normalized probability density function beingthereby created,

iii) feeding information, which represents said normalized probabilitydensity function, into a neural network such that a predetermined value,which falls within the range of the maximum value and the minimum valueof the image signal in said normalized probability density function, mayalways be fed into the same input unit of said neural network,

iv) feeding out information representing the read-out conditions and/orthe image processing conditions from said neural network,

v) correcting the read-out conditions and/or the image processingconditions, which are represented by said information fed out from saidneural network, on the basis of said predetermined value, and

vi) thereby adjusting the final read-out conditions and/or the finalimage processing conditions.

The present invention still further provides, as a fourteenth method, amethod for adjusting read-out conditions and/or image processingconditions for a radiation image, wherein a first image signalrepresenting a radiation image of an object is obtained by exposing astimulable phosphor sheet, on which the radiation image has been stored,to stimulating rays, which cause the stimulable phosphor sheet to emitlight in proportion to the amount of energy stored thereon during itsexposure to radiation, the emitted light being detected,

a second image signal representing the radiation image is thereafterobtained by again exposing the stimulable phosphor sheet to stimulatingrays, the light emitted by the stimulable phosphor sheet being detected,and

read-out conditions, under which the second image signal is to beobtained, and/or image processing conditions, under which the secondimage signal having been obtained is to be image processed, are adjustedon the basis of the first image signal,

the method for adjusting read-out conditions and/or image processingconditions for a radiation image comprising the steps of:

i) taking the value of said first image signal, which value representsthe maximum amount of the emitted light in part of a probability densityfunction of said first image signal other than the part corresponding toa background region in said radiation image, as the maximum value,

ii) normalizing said probability density function with its maximum valuein its part between said maximum value and the minimum value of saidfirst image signal, a normalized probability density function beingthereby created,

iii) feeding information, which represents said normalized probabilitydensity function, and subsidiary information, which gives specificsabout said radiation image stored on said stimulable phosphor sheet,into a neural network such that a predetermined value, which fallswithin the range of the maximum value and the minimum value of the imagesignal in said normalized probability density function, may always befed into the same input unit of said neural network,

iv) feeding out information representing the read-out conditions and/orthe image processing conditions from said neural network,

v) correcting the read-out conditions and/or the image processingconditions, which are represented by said information fed out from saidneural network, on the basis of said predetermined value, and

vi) thereby adjusting the final read-out conditions and/or the finalimage processing conditions.

The present invention also provides, as an eleventh apparatus, anapparatus for adjusting read-out conditions and/or image processingconditions for a radiation image, wherein a first image signalrepresenting a radiation image of an object is obtained by exposing astimulable phosphor sheet, on which the radiation image has been stored,to stimulating rays, which cause the stimulable phosphor sheet to emitlight in proportion to the amount of energy stored thereon during itsexposure to radiation, the emitted light being detected,

a second image signal representing the radiation image is thereafterobtained by again exposing the stimulable phosphor sheet to stimulatingrays, the light emitted by the stimulable phosphor sheet being detected,and

read-out conditions, under which the second image signal is to beobtained, and/or image processing conditions, under which the secondimage signal having been obtained is to be image processed, are adjustedon the basis of the first image signal,

the apparatus for adjusting read-out conditions and/or image processingconditions for a radiation image comprising:

i) a probability density function creating means for creating aprobability density function of said first image signal and feeding outinformation, which represents said probability density function, and

ii) a neural network for receiving said information, which representssaid probability density function, from said probability densityfunction creating means, adjusting the read-out conditions and/or theimage processing conditions on the basis of said probability densityfunction, and feeding out information representing the read-outconditions and/or the image processing conditions, which have thus beenadjusted.

The present invention further provides, as a twelfth apparatus, anapparatus for adjusting read-out conditions and/or image processingconditions for a radiation image, wherein a first image signalrepresenting a radiation image of an object is obtained by exposing astimulable phosphor sheet, on which the radiation image has been stored,to stimulating rays, which cause the stimulable phosphor sheet to emitlight in proportion to the amount of energy stored thereon during itsexposure to radiation, the emitted light being detected,

a second image signal representing the radiation image is thereafterobtained by again exposing the stimulable phosphor sheet to stimulatingrays, the light emitted by the stimulable phosphor sheet being detected,and

read-out conditions, under which the second image signal is to beobtained, and/or image processing conditions, under which the secondimage signal having been obtained is to be image processed, are adjustedon the basis of the first image signal,

the apparatus for adjusting read-out conditions and/or image processingconditions for a radiation image comprising:

i) a probability density function creating means for creating aprobability density function of said first image signal and feeding outinformation, which represents said probability density function,

ii) a subsidiary information feed-out means for feeding out subsidiaryinformation, which gives specifics about said radiation image stored onsaid stimulable phosphor sheet, and

iii) a neural network for receiving said information, which representssaid probability density function, from said probability densityfunction creating means, receiving said subsidiary information from saidsubsidiary information feed-out means, adjusting the read-out conditionsand/or the image processing conditions on the basis of said probabilitydensity function and said subsidiary information, and feeding outinformation representing the read-out conditions and/or the imageprocessing conditions, which have thus been adjusted.

The present invention still further provides, as a thirteenth apparatus,an apparatus for adjusting read-out conditions and/or image processingconditions for a radiation image, wherein a first image signalrepresenting a radiation image of an object is obtained by exposing astimulable phosphor sheet, on which the radiation image has been stored,to stimulating rays, which cause the stimulable phosphor sheet to emitlight in proportion to the amount of energy stored thereon during itsexposure to radiation, the emitted light being detected,

a second image signal representing the radiation image is thereafterobtained by again exposing the stimulable phosphor sheet to stimulatingrays, the light emitted by the stimulable phosphor sheet being detected,and

read-out conditions, under which the second image signal is to beobtained, and/or image processing conditions, under which the secondimage signal having been obtained is to be image processed, are adjustedon the basis of the first image signal,

the apparatus for adjusting read-out conditions and/or image processingconditions for a radiation image comprising:

i) an operation means for creating a probability density function ofsaid first image signal, detecting the value of said first image signal,which value represents the maximum amount of the emitted light in partof a probability density function of said first image signal other thanthe part corresponding to a background region in said radiation image,taking said value of said first image signal, which has thus beendetected from said probability density function, as the maximum value,normalizing said probability density function with its maximum value inits part between said maximum value and the minimum value of said firstimage signal, a normalized probability density function being therebycreated, and feeding out information representing said normalizedprobability density function,

ii) a neural network for receiving said information, which representssaid normalized probability density function, from said operation meanssuch that a predetermined value, which falls within the range of themaximum value and the minimum value of the image signal in saidnormalized probability density function, may always be fed into the sameinput unit of said neural network, determining the read-out conditionsand/or the image processing conditions on the basis of said normalizedprobability density function, and feeding out information representingthe read-out conditions and/or the image processing conditions, whichhave thus been determined, and

iii) a correction means for correcting the read-out conditions and/orthe image processing conditions, which are represented by saidinformation fed out from said neural network, on the basis of saidpredetermined value.

The present invention also provides, as a fourteenth apparatus, anapparatus for adjusting read-out conditions and/or image processingconditions for a radiation image, wherein a first image signalrepresenting a radiation image of an object is obtained by exposing astimulable phosphor sheet, on which the radiation image has been stored,to stimulating rays, which cause the stimulable phosphor sheet to emitlight in proportion to the amount of energy stored thereon during itsexposure to radiation, the emitted light being detected,

a second image signal representing the radiation image is thereafterobtained by again exposing the stimulable phosphor sheet to stimulatingrays, the light emitted by the stimulable phosphor sheet being detected,and

read-out conditions, under which the second image signal is to beobtained, and/or image processing conditions, under which the secondimage signal having been obtained is to be image processed, are adjustedon the basis of the first image signal,

the apparatus for adjusting read-out conditions and/or image processingconditions for a radiation image comprising:

i) an operation means for creating a probability density function ofsaid first image signal, detecting the value of said first image signal,which value represents the maximum amount of the emitted light in partof a probability density function of said first image signal other thanthe part corresponding to a background region in said radiation image,taking said value of said first image signal, which has thus beendetected from said probability density function, as the maximum value,normalizing said probability density function with its maximum value inits part between said maximum value and the minimum value of said firstimage signal, a normalized probability density function being therebycreated, and feeding out information representing said normalizedprobability density function,

ii) a subsidiary information feed-out means for feeding out subsidiaryinformation, which gives specifics about said radiation image stored onsaid stimulable phosphor sheet,

iii) a neural network for receiving said information, which representssaid normalized probability density function, from said operation means,and said subsidiary information from said subsidiary informationfeed-out means such that a predetermined value, which falls within therange of the maximum value and the minimum value of the image signal insaid normalized probability density function, may always be fed into thesame input unit of said neural network, determining the read-outconditions and/or the image processing conditions on the basis of saidnormalized probability density function, and feeding out informationrepresenting the read-out conditions and/or the image processingconditions, which have thus been determined, and

iv) a correction means for correcting the read-out conditions and/or theimage processing conditions, which are represented by said informationfed out from said neural network, on the basis of said predeterminedvalue.

A fifteenth method in accordance with the present invention isapplicable when a stimulable phosphor sheet and other recording mediaare used and the image processing conditions are adjusted.

Specifically, the present invention further provides, as a fifteenthmethod, a method for adjusting image processing conditions for aradiation image, wherein image processing conditions, under which animage signal is to be image processed, are adjusted on the basis of theimage signal representing a radiation image of an object,

the method for adjusting image processing conditions for a radiationimage comprising the steps of:

i) feeding information, which represents a probability density functionof said image signal, into a neural network, and

ii) feeding out information representing the image processing conditionsfrom said neural network.

The present invention still further provides, as a fifteenth apparatus,an apparatus for adjusting image processing conditions for a radiationimage, wherein image processing conditions, under which an image signalis to be image processed, are adjusted on the basis of the image signalrepresenting a radiation image of an object,

the apparatus for adjusting image processing conditions for a radiationimage comprising:

i) a probability density function creating means for creating aprobability density function of said image signal and feeding outinformation, which represents said probability density function, and

ii) a neural network for receiving said information, which representssaid probability density function, from said probability densityfunction creating means, adjusting the image processing conditions onthe basis of said probability density function, and feeding outinformation representing the image processing conditions, which havethus been adjusted.

The present invention also provides, as a sixteenth method, a method foradjusting image processing conditions for a radiation image, whereinimage processing conditions, under which an image signal is to be imageprocessed, are adjusted on the basis of the image signal representing aradiation image of an object,

the method for adjusting image processing conditions for a radiationimage comprising the steps of:

i) feeding information, which represents a probability density functionof said image signal, and subsidiary information, which gives specificsabout said radiation image, into a neural network, and

ii) feeding out information representing the image processing conditionsfrom said neural network.

The present invention further provides, as a sixteenth apparatus, anapparatus for adjusting image processing conditions for a radiationimage, wherein image processing conditions, under which an image signalis to be image processed, are adjusted on the basis of the image signalrepresenting a radiation image of an object,

the apparatus for adjusting image processing conditions for a radiationimage comprising:

i) a probability density function creating means for creating aprobability density function of said image signal and feeding outinformation, which represents said probability density function,

ii) a subsidiary information feed-out means for feeding out subsidiaryinformation, which gives specifics about said radiation image, and

iii) a neural network for receiving said information, which representssaid probability density function, from said probability densityfunction creating means, receiving said subsidiary information from saidsubsidiary information feed-out means, adjusting the image processingconditions on the basis of said probability density function and saidsubsidiary information, and feeding out information representing theimage processing conditions, which have thus been adjusted.

The present invention still further provides, as a seventeenth method, amethod for adjusting image processing conditions for a radiation image,wherein image processing conditions, under which an image signal is tobe image processed, are adjusted on the basis of the image signalrepresenting a radiation image of an object,

the method for adjusting image processing conditions for a radiationimage comprising the steps of:

i) taking the value of said image signal, which value represents themaximum amount of the emitted light in part of a probability densityfunction of said image signal other than the part corresponding to abackground region in said radiation image, as the maximum value,

ii) normalizing said probability density function with its maximum valuein its part between said maximum value and the minimum value of saidimage signal, a normalized probability density function being therebycreated,

iii) feeding information, which represents said normalized probabilitydensity function, into a neural network such that a predetermined value,which falls within the range of the maximum value and the minimum valueof the image signal in said normalized probability density function, mayalways be fed into the same input unit of said neural network,

iv) feeding out information representing the image processing conditionsfrom said neural network,

v) correcting the image processing conditions, which are represented bysaid information fed out from said neural network, on the basis of saidpredetermined value, and

vi) thereby adjusting the final image processing conditions.

The present invention also provides, as a seventeenth apparatus, anapparatus for adjusting image processing conditions for a radiationimage, wherein image processing conditions, under which an image signalis to be image processed, are adjusted on the basis of the image signalrepresenting a radiation image of an object,

the apparatus for adjusting image processing conditions for a radiationimage comprising:

i) an operation means for creating a probability density function ofsaid image signal, detecting the value of said image signal, which valuerepresents the maximum amount of the emitted light in part of aprobability density function of said image signal other than the partcorresponding to a background region in said radiation image, takingsaid value of said image signal, which has thus been detected from saidprobability density function, as the maximum value, normalizing saidprobability density function with its maximum value in its part betweensaid maximum value and the minimum value of said image signal, anormalized probability density function being thereby created, andfeeding out information representing said normalized probability densityfunction,

ii) a neural network for receiving said information, which representssaid normalized probability density function, from said operation meanssuch that a predetermined value, which falls within the range of themaximum value and the minimum value of the image signal in saidnormalized probability density function, may always be fed into the sameinput unit of said neural network, determining the image processingconditions on the basis of said normalized probability density function,and feeding out information representing the image processingconditions, which have thus been determined, and

iii) a correction means for correcting the image processing conditions,which are represented by said information fed out from said neuralnetwork, on the basis of said predetermined value.

The present invention further provides, as an eighteenth method, amethod for adjusting image processing conditions for a radiation image,wherein image processing conditions, under which an image signal is tobe image processed, are adjusted on the basis of the image signalrepresenting a radiation image of an object,

the method for adjusting image processing conditions for a radiationimage comprising the steps of:

i) taking the value of said image signal, which value represents themaximum amount of the emitted light in part of a probability densityfunction of said image signal other than the part corresponding to abackground region in said radiation image, as the maximum value,

ii) normalizing said probability density function with its maximum valuein its part between said maximum value and the minimum value of saidimage signal, a normalized probability density function being therebycreated,

iii) feeding information, which represents said normalized probabilitydensity function, and subsidiary information, which gives specificsabout said radiation image, into a neural network such that apredetermined value, which falls within the range of the maximum valueand the minimum value of the image signal in said normalized probabilitydensity function, may always be fed into the same input unit of saidneural network,

iv) feeding out information representing the image processing conditionsfrom said neural network,

v) correcting the image processing conditions, which are represented bysaid information fed out from said neural network, on the basis of saidpredetermined value, and

vi) thereby adjusting the final image processing conditions.

The present invention also provides, as an eighteenth apparatus, anapparatus for adjusting image processing conditions for a radiationimage, wherein image processing conditions, under which an image signalis to be image processed, are adjusted on the basis of the image signalrepresenting a radiation image of an object,

the apparatus for adjusting image processing conditions for a radiationimage comprising:

i) an operation means for creating a probability density function ofsaid image signal, detecting the value of said image signal, which valuerepresents the maximum amount of the emitted light in part of aprobability density function of said image signal other than the partcorresponding to a background region in said radiation image, takingsaid value of said image signal, which has thus been detected from saidprobability density function, as the maximum value, normalizing saidprobability density function with its maximum value in its part betweensaid maximum value and the minimum value of said image signal, anormalized probability density function being thereby created, andfeeding out information representing said normalized probability densityfunction,

ii) a subsidiary information feed-out means for feeding out subsidiaryinformation, which gives specifics about said radiation image,

iii) a neural network for receiving said information, which representssaid normalized probability density function, from said operation means,and said subsidiary information from said subsidiary informationfeed-out means such that a predetermined value, which falls within therange of the maximum value and the minimum value of the image signal insaid normalized probability density function, may always be fed into thesame input unit of said neural network, determining the image processingconditions on the basis of said normalized probability density function,and feeding out information representing the image processingconditions, which have thus been determined, and

iv) a correction means for correcting the image processing conditions,which are represented by said information fed out from said neuralnetwork, on the basis of said predetermined value.

The predetermined value, which falls within the range of the maximumvalue and the minimum value of the image signal in the normalizedprobability density function, may be any value falling within the rangeof the maximum value and the minimum value of the image signal. By wayof example, the predetermined value may be the maximum value, theminimum value, or an intermediate value between the maximum value andthe minimum value of the image signal. The information representing thenormalized probability density function is fed into the neural networksuch that the predetermined value may always be fed into the same inputunit of the neural network. This means that, even if the values of theimage signal and the predetermined value change, the aforesaidpredetermined value is always fed into a certain predetermined inputunit of the neural network. Values of the image signal other than theaforesaid predetermined value are sequentially fed into units adjacentto the predetermined input unit with reference to the aforesaidpredetermined value.

With the eleventh and twelfth apparatuses in accordance with the presentinvention, the information, which represents the probability densityfunction of the image signal, is fed into the neural network.Alternatively, both the information, which represents the probabilitydensity function of the image signal, and the subsidiary information,which gives specifics about the radiation image, such as the informationconcerning the patient and the mode in which the radiation image wasrecorded, are fed into the neural network. The neural network adjuststhe read-out conditions for the final readout and/or the imageprocessing conditions on the basis of the probability density function,or on the basis of both the probability density function and thesubsidiary information. Therefore, by using the neural network capableof making general judgments, drawbacks of a method utilizing only theprobability density function of an image signal can be eliminated (i.e.errors due to local analysis can be eliminated). Also, the read-outconditions for the final readout and/or the image processing conditionscan be adjusted efficiently and accurately, the learning of the neuralnetwork being repeated only a few times.

With the thirteenth and fourteenth apparatuses in accordance with thepresent invention, the value of the image signal, which value representsthe maximum amount of the emitted light in part of a probability densityfunction of the image signal other than the part corresponding to abackground region in the radiation image, is taken as the maximum value.The probability density function is normalized with its maximum value inits part between the maximum value and the minimum value of the imagesignal, a normalized probability density function being thereby created.The read-out conditions for the final readout and/or the imageprocessing conditions are determined on the basis of the normalizedprobability density function and are then corrected. Therefore,appropriate read-out conditions for the final readout and/or appropriateimage processing conditions, can be adjusted on the basis of theprobability density function of the image signal components of the imagesignal, which correspond only to the object image region in theradiation image. The conditions thus adjusted are not adversely affectedby the image signal components of the image signal corresponding to thebackground region in the radiation image.

Also, the image signal representing a radiation image is not always thesame. When the radiation dose varies, the sensitivity also varies, andtherefore the values of the image signal change. Specifically, when thesensitivity varies, the minimum value of the image signal and themaximum value thereof in the part of the probability density function ofthe image signal other than the part corresponding to a backgroundregion in the radiation image also change. With the thirteenth andfourteenth apparatuses in accordance with the present invention, theinformation representing the normalized probability density function isfed into the neural network such that the predetermined value, whichfalls within the range of the maximum value and the minimum value of theimage signal in the normalized probability density function, may alwaysbe fed into the same input unit of the neural network. The read-outconditions and/or the image processing conditions, which are representedby the information fed out from the neural network, are corrected on thebasis of the predetermined value, which falls within the range of themaximum value and the minimum value of the image signal in thenormalized probability density function. Therefore, appropriate read-outconditions for the final readout and/or appropriate image processingconditions can be obtained ultimately, which are not affected by thesensitivity of the radiation image.

Also, with the thirteenth and fourteenth apparatuses in accordance withthe present invention, the information, which represents the normalizedprobability density function, or both the normalized probability densityfunction and the subsidiary information, which gives specifics about theradiation image, such as the information concerning the patient and themode in which the radiation image was recorded, are fed into the neuralnetwork. The read-out conditions for the final readout and/or the imageprocessing conditions are determined by the neural network on the basisof the normalized probability density function, or on the basis of boththe normalized probability density function and the subsidiaryinformation, and are then corrected on the basis of the predeterminedvalue. Therefore, by using the neural network capable of making generaljudgments, drawbacks of a method utilizing only the probability densityfunction of an image signal can be eliminated (i.e. errors due to localanalysis can be eliminated). Also, the read-out conditions for the finalreadout and/or the image processing conditions can be adjustedefficiently and accurately, the learning of the neural network beingrepeated only a few times.

The same effects as those described above can be obtained also with thefifteenth through eighteenth apparatuses in accordance with the presentinvention.

The present invention still further provides, as a nineteenth apparatus,a radiation image analyzing apparatus comprising:

i) an irradiation field determining means for determining the shape andlocation of an irradiation field of radiation in a radiation image onthe basis of a plurality of image signal components representing pictureelements in said radiation image, which includes the irradiation fieldat a part, and

ii) a characteristic measure operating means provided with a neuralnetwork, which receives all or some of the image signal componentsrepresenting the picture elements located in the irradiation fieldhaving been determined and feeds out information representingcharacteristic measures, the characteristic measures representing thecharacteristics of said radiation image.

No limitation is imposed on the algorithms, which are employed in theirradiation field determining means to determine the shape and locationof the irradiation field. For example, algorithms may be employed whichdetermine the shape and location of the irradiation field on the basisof a difference between the mean-level value of the values of the imagesignal components corresponding to the region inside of the irradiationfield and the mean-level value of the values of the image signalcomponents corresponding to the region outside of the irradiation field.Alternatively, algorithms may be employed which determine the shape andlocation of the irradiation field on the basis of a difference betweenthe amount of dispersion in the values of the image signal componentscorresponding to the region inside of the irradiation field and theamount of dispersion in the values of the image signal componentscorresponding to the region outside of the irradiation field. As anotheralternative, algorithms may be employed which determine the shape andlocation of the irradiation field on the basis of how the value of theimage signal changes in the vicinity of the contour of the irradiationfield. As a further alternative, algorithms may be employed whichdetermine the shape and location of the irradiation field on the basisof a combination of two or more of these factors. Such algorithms aredescribed in, for example, Japanese Unexamined Patent Publication Nos.61(1986)-39039, 63(1988)-259538, 1(1989)-42436, and 2(1990)-67690.

Also, no limitation is imposed on the characteristic measures. Forexample, the characteristic measures may be the read-out conditions forthe final readout, which are adjusted to appropriate values, the imageprocessing conditions, under which the image signal representing theradiation image is to be image processed, the portion of the object theimage of which was recorded (e.g., the head, the neck, the chest, or theabdomen in cases where the object is a human body), and the orientationin which the object was placed when the image of the object was recorded(e.g. the frontal or side orientation).

With the nineteenth apparatus in accordance with the present invention,the shape and location of the irradiation field are determined, and onlythe image signal components representing the picture elements located inthe irradiation field are fed into the neural network. Therefore, thenumber of the input points of the neural network and the number of theunits thereof can be kept small. Also, a storage means having a smallstorage capacity may be used to store information representing thecoefficients concerning the weight of connections between the units.Additionally, by carrying out the learning of the neural network foronly a short period, accurate outputs can be obtained from the neuralnetwork.

Instead of all of the image signal components representing the pictureelements located in the irradiation field being fed into the neuralnetwork, only some of these image signal components may be fed into theneural network. For example, these image signal components may besampled alternately, and only the sampled components may be fed into theneural network. In such cases, a storage means having an even smallerstorage capacity may be employed.

The present invention also provides, as a nineteenth method, a radiationimage analyzing method, wherein a subdivision pattern of radiationimages, the shape and location of an irradiation field, an orientationin which an object was placed when the image of the object was recorded,and/or a portion of an object the image of which was recorded isdetermined on the basis of an image signal representing a radiationimage of the object,

the radiation image analyzing method comprising the steps of:

i) feeding the image signal into a neural network, and

ii) feeding out information, which represents the results of thedetermination with respect to the radiation image, from said neuralnetwork.

The present invention further provides, as a twentieth apparatus, aradiation image analyzing apparatus comprising:

i) an image signal feed-out means for feeding out an image signalrepresenting a radiation image of an object, and

ii) a characteristic measure operating means provided with a neuralnetwork, which receives said image signal and feeds out informationrepresenting characteristic measures, said characteristic measuresrepresenting the results of determination of a subdivision pattern ofradiation images, the shape and location of an irradiation field, anorientation in which an object was placed when the image of the objectwas recorded, and/or a portion of an object the image of which wasrecorded.

No limitation is imposed on the characteristic measures. For example, incases where a subdivision image recording was carried out, in which therecording area of a recording media is divided into a plurality ofpredetermined subdivisions and a radiation image is recorded in each ofthe subdivisions, the subdivision pattern is determined. In such cases,the characteristic measures may represent the results of determinationof four subdivision patterns (a two-on-one subdivision pattern havingtwo radiation images which are vertically adjacent to each other, atwo-on-one subdivision pattern having two radiation images which arehorizontally adjacent to each other, a four-on-one subdivision patternhaving four radiation images which are vertically and horizontallyadjacent to each other, and one-on-one pattern). As for thedetermination of the shape and location of an irradiation field, thecharacteristic measures may represent the results of determination ofthe contour of an irradiation field, e.g. a circular irradiation fieldor a rectangular irradiation field. In the determination of theorientation in which the object was placed when the image of the objectwas recorded, the characteristic measures may represent the results ofdetermination of two orientations, e.g. a frontal orientation (a frontalimage) and a side orientation (a side image). As for the determinationof the portion of the object the image of which was recorded, thecharacteristic measures may represent the head, the chest, the shoulder,the arm, or the like.

With the nineteenth method and the twentieth apparatus in accordancewith the present invention, the neural network receives the imagesignal, which is made up of a series of image signal componentsrepresenting the picture elements in a radiation image, and feeds outthe information representing the characteristic measures. Thecharacteristic measures represent the results of determination of asubdivision pattern of radiation images, the shape and location of anirradiation field, an orientation in which an object was placed when theimage of the object was recorded, and/or a portion of an object theimage of which was recorded. Therefore, determination can be carried outaccurately with a simple apparatus.

Instead of all of the image signal components representing the pictureelements located in the irradiation field being fed into the neuralnetwork, only some of these image signal components may be fed into theneural network. For example, these image signal components may besampled alternately, and only the sampled components may be fed into theneural network. In such cases, a storage means having an even smallerstorage capacity may be employed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B are explanatory views showing X-ray images of the rightand left shoulders,

FIGS. 2A and 2B are explanatory views showing a standard pattern and areversed pattern,

FIG. 3 is an explanatory view showing an example of a neural network,

FIG. 4 is a schematic view showing an example of an X-ray imagerecording apparatus,

FIG. 5 is a perspective view showing an example of an X-ray imageread-out apparatus and an example of a computer system, in which anembodiment of the first apparatus in accordance with the presentinvention is employed,

FIGS. 6A and 6B are explanatory views showing X-ray images of theshoulder joint,

FIGS. 7A and 7B are graphs showing patterns of probability densityfunctions of image signals, which represent the X-ray images shown inFIGS. 6A and 6B,

FIGS. 8, 9, and 10 are block diagrams showing systems for carrying outdifferent embodiments of the method in accordance with the presentinvention,

FIG. 11 is a block diagram showing a different embodiment of the methodin accordance with the present invention,

FIG. 12 is a block diagram showing a different embodiment of the methodin accordance with the present invention,

FIGS. 13A and 13B are explanatory views showing examples of X-raysstored on stimulable phosphor sheets,

FIG. 14 is a block diagram showing how the read-out conditions for thefinal readout are adjusted,

FIG. 15 is a graph showing an example of a probability density functionof a preliminary read-out image signal,

FIG. 16 is a perspective view showing another embodiment of theradiation image read-out apparatus in accordance with the presentinvention,

FIG. 17 is a block diagram showing a different embodiment of the methodfor adjusting read-out conditions and/or image processing conditions fora radiation image in accordance with the present invention,

FIGS. 18A and 18B are explanatory views showing two images in which thepositions of center points of the patterns of an object shift from eachother,

FIG. 19 is a block diagram showing a different embodiment of the methodfor adjusting read-out conditions and/or image processing conditions fora radiation image in accordance with the present invention,

FIG. 20 is a graph showing examples of probability density functions ofimage signals detected from stimulable phosphor sheets,

FIG. 21 is a block diagram showing a different embodiment of the methodfor adjusting read-out conditions and/or image processing conditions fora radiation image in accordance with the present invention,

FIG. 22 is an explanatory view showing a normalized probability densityfunction of a preliminary read-out image signal and an example of aneural network,

FIG. 23 is a graph showing different examples of probability densityfunctions of image signals detected from stimulable phosphor sheets,

FIG. 24 is an explanatory view showing an example of an X-ray image, apreliminary read-out image signal representing the X-ray image, anddifferentiated values of the preliminary read-out image signal,

FIG. 25 is an explanatory graph showing how straight lines, whichconnect contour points of an irradiation field, are detected,

FIG. 26 is an explanatory view showing how a region surrounded bystraight lines, which connect contour points of an irradiation field, isextracted,

FIG. 27 is an explanatory view showing some of picture elements in anX-ray image, which are located in an irradiation field,

FIG. 28 is a block diagram showing the major part of an embodiment ofthe radiation image analyzing apparatus in accordance with the presentinvention,

FIGS. 29A, 29B, and 29C are explanatory views showing how isolatedpoints are eliminated by a figure fusing process,

FIGS. 30A, 30B, and 30C are explanatory views showing how missing pointsare eliminated by a figure fusing process,

FIG. 31 is a block diagram showing an example of how a binary imageprocess is carried out on a binary pattern signal, which is fed out froma neural network and represents the shape and location of an irradiationfield, and

FIG. 32 is a block diagram showing a different example of how a binaryimage process is carried out on a binary pattern signal, which is fedout from a neural network and represents the shape and location of anirradiation field.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention will hereinbelow be described in further detailwith reference to the accompanying drawings.

In the embodiments described below, an X-ray image of the shoulder of ahuman body is stored on a stimulable phosphor sheet.

FIG. 4 is a schematic view showing an example of an X-ray imagerecording apparatus.

With reference to FIG. 4, X-rays 3 are produced by an X-ray source 2 ofan X-ray image recording apparatus 1 and irradiated to the shoulder 4aof a human body 4. X-rays 3a, which have passed through the human body4, impinge upon a stimulable phosphor sheet 11. In this manner, an X-rayimage of the shoulder 4a of the human body 4 is stored on the stimulablephosphor sheet 11.

FIGS. 1A and 1B are explanatory views showing examples of X-ray imagesof the shoulders stored on stimulable phosphor sheets.

FIGS. 1A and 1B show the X-ray images of the right and left shoulders.Each of the X-ray images comprises an object image region 5, in whichthe pattern of the human body is stored, and a background region 6, uponwhich the X-rays impinged directly without passing through the object 4.

FIG. 5 is a perspective view showing an example of an X-ray imageread-out apparatus and an example of a computer system, in which anembodiment of the first apparatus in accordance with the presentinvention is employed. In this embodiment, by way of example, astimulable phosphor sheet is used, and a preliminary readout is carriedout.

The stimulable phosphor sheet 11, on which the X-ray image has beenstored, is placed at a predetermined position in a preliminary read-outmeans 100 which carries out a preliminary readout by scanning thestimulable phosphor sheet 11 with a light beam having a low energylevel, thereby releasing only part of the energy from the stimulablephosphor sheet 11, which energy was stored during its exposure toradiation. The stimulable phosphor sheet 11 is conveyed in asub-scanning direction indicated by the arrow Y by a sheet conveyancemeans 13 which is constituted of an endless belt or the like and whichis operated by a motor 12. A laser beam 15 which has a low energy levelis produced by a laser beam source 14, and is reflected and deflected bya rotating polygon mirror 16 which is quickly rotated by a motor 23 inthe direction indicated by the arrow. The laser beam 15 then passesthrough a converging lens 17 constituted of an fθ lens or the like. Thedirection of the optical path of the laser beam 15 is then changed by amirror 18, and the laser beam 15 impinges upon the stimulable phosphorsheet 11 and scans it in a main scanning direction indicated by thearrow X, which direction is approximately normal to the sub-scanningdirection indicated by the arrow Y. When the stimulable phosphor sheet11 is exposed to the laser beam 15, the exposed portion of thestimulable phosphor sheet 11 emits light 19 in an amount proportional tothe amount of energy stored thereon during its exposure to radiation.The emitted light 19 is guided by a light guide member 20 andphotoelectrically detected by a photomultiplier 21. The light guidemember 20 is made from a light guiding material such as an acrylic plateand has a linear light input face 20a, positioned so that it extendsalong the main scanning line on the stimulable phosphor sheet 11, and aring-shaped light output face 20b, positioned so that it is in closecontact with a light receiving face of the photomultiplier 21. Theemitted light 19, which has entered the light guide member 20 at itslight input face 20a, is guided through repeated total reflection insideof the light guide member 20, emanates from the light output face 20b,and is received by the photomultiplier 21. In this manner, the amount ofthe emitted light 19, which amount represents the X-ray image, isconverted into an electric signal by the photomultiplier 21.

An analog output signal S generated by the photomultiplier 21 islogarithmically amplified by a logarithmic amplifier 26, and digitizedby an A/D converter 27 into a preliminary read-out image signal SP. Thepreliminary read-out image signal SP takes a value proportional to thelogarithmic value of the amount of the light 19, which was emitted fromeach of picture elements in the X-ray image stored on the stimulablephosphor sheet 11.

In the preliminary readout, read-out conditions, i.e. the voltageapplied to the photomultiplier 21 and the amplification factor of thelogarithmic amplifier 26, are adjusted so that image information can bedetected accurately even if the amount of energy stored on thestimulable phosphor sheet 11 during its exposure to radiation variesover a wide range.

The preliminary read-out image signal SP obtained in the mannerdescribed above is fed into a computer system 40. The computer system 40is provided with an embodiment of the first apparatus in accordance withthe present invention. The computer system 40 comprises a main body 41in which a CPU and an internal memory are incorporated, a disk driveunit 42 which operates a floppy disk serving as a subsidiary memory, akeyboard 43 from which necessary instructions, or the like, are fed intothe computer system 40, and a CRT display device 44 which displaysnecessary information.

In the computer system 40, the read-out conditions for the finalreadout, i.e. the sensitivity and the contrast during the final readout,are determined in the manner described later. By way of example, thevoltage applied to a photomultiplier 21' and the amplification factor ofa logarithmic amplifier 26' are controlled in accordance with thesensitivity and the contrast.

The contrast corresponds to the ratio of the largest amount of emittedlight, which is capable of being accurately converted into an imagesignal during the final readout, to the smallest amount of emittedlight, which is capable of being accurately converted into an imagesignal during the final readout. The sensitivity corresponds to thephotoelectric conversion factor, which represents to what image signallevel a predetermined amount of emitted light is to be converted.

A stimulable phosphor sheet 11' on which the preliminary readout hasbeen finished is placed at a predetermined position in the finalread-out means 100' and scanned with a laser beam 15' having an energylevel higher than that of the laser beam 15 used during the preliminaryreadout. In this manner, an image signal is detected under the read-outconditions which have been determined on the basis of the preliminaryread-out image signal. The configuration of the final read-out means100' is nearly the same as that of the preliminary read-out means 100,and therefore elements corresponding to those constituting thepreliminary read-out means 100 are numbered with corresponding primedreference numerals in FIG. 5.

After the image signal is digitized in an A/D converter 27', theresulting image signal SQ is fed into the computer system 40, whichcarries out appropriate image processing on the image signal SQ. Afterbeing image processed, the image signal is fed into a reproducingapparatus (not shown), which reproduces a visible image from the imagesignal.

How the computer system 40 adjusts the read-out conditions for the finalreadout on the basis of the preliminary read-out image signal SP will bedescribed hereinbelow.

As shown in FIGS. 1A and 1B, during the recording of X-ray images of theshoulder, the images which are reversed approximately horizontally areoften obtained. In such cases, a judgment is made in the mannerdescribed below as to whether the image is of the right shoulder (FIG.1A) or of the left shoulder (FIG. 1B). In this embodiment, the image ofthe right shoulder shown in FIG. 1A is taken as the standard pattern inthe first apparatus in accordance with the present invention.

FIGS. 2A and 2B are explanatory views showing a standard pattern and areversed pattern, which are represented by information stored in thecomputer system 40.

The standard pattern is composed of a first region 7, which isrepresented by a mean-level value of the preliminary read-out imagesignal SP corresponding to the object image region in the X-ray imageshown in FIG. 1A, and a second region 8, which is represented by amean-level value of the preliminary read-out image signal SP detectedfrom the background region 6 in the X-ray image shown in FIG. 1A. Also,the reversed pattern is composed of a first region 7, which isrepresented by a mean-level value of the preliminary read-out imagesignal SP corresponding to the object image region in the X-ray imageshown in FIG. 1B, and a second region 8, which is represented by amean-level value of the preliminary read-out image signal SP detectedfrom the background region 6 in the X-ray image shown in FIG. 1B.

When the preliminary read-out image signal SP is fed into the computersystem 40, pattern matching is carried out between the preliminaryread-out image signal SP and each of the image signal SS representingthe standard pattern shown in FIG. 2A and the image signal SRrepresenting the reversed pattern shown in FIG. 2B. In this manner, ajudgment is made as to whether the preliminary read-out image signal SPrepresents the X-ray image of the right shoulder or of the leftshoulder. In this embodiment, during the pattern matching, calculationsare made to find square values of differences between the image signalcomponents of the preliminary read-out image signal SP and each of theimage signals SS and SR, which image signal components representcorresponding picture elements in the preliminary read-out image signalSP and each of the image signals SS and SR, i.e. (SS-SP)² and (SR-SP)².The square values obtained for the whole area of the image are added,and sums QS and QR are calculated with the formulas

    Q.sub.S =Σ(S.sub.S -S.sub.p).sup.2                   (1)

    Q.sub.R =Σ(S.sub.R -S.sub.p).sup.2                   (2)

It is judged that the X-ray image represented by the preliminaryread-out image signal SP is the image associated with the sums QS or QR,whichever is smaller.

In cases where it has been judged that the image represented by thepreliminary read-out image signal SP is of the left shoulder (FIG. 1B),the preliminary read-out image signal SP is processed such that theimage represented by the preliminary read-out image signal SP isreversed. In this manner, an image signal corresponding to the image ofthe right shoulder shown in FIG. 1A, or an image signal corresponding tothe image of the left shoulder is always fed into a neural network,which will be described below.

In the manner described above, the image signal is processed such thatthe processed image signal represents the predetermined standard pattern(i.e. the pattern of the right shoulder in this embodiment). Theprocessed image signal is then fed into the neural network. Therefore,the number of units constituting the neural network can be reduced, andthe requirement of the storage capacity of a storage means for storingthe weight coefficients, which represent the degrees of connectionsbetween the units, can be kept small. Also, the learning of the neuralnetwork can be finished quickly.

FIG. 3 is an explanatory view showing an example of the neural networkwhich is provided with a learning function by back propagation method.As described above, the term "learning function by back propagationmethod" as used herein means the learning algorithms in a neuralnetwork, with which the output of the neural network is compared with acorrect answer (an instructor signal), and the weight of connections(i.e. the weight of synapse connections) is corrected sequentially fromthe output side to the input side of the neural network.

With reference to FIG. 3, the neural network comprises a first layer (aninput layer), a second layer (an intermediate layer), and a third layer(an output layer). The first, second, and third layers are composedrespectively of n1 number of units, n2 number of units, and two units.Signals F1, F2, . . . , Fn1 fed into the first layer (the input layer)are the image signal components of the preliminary read-out image signalSP representing the picture elements in the X-ray image (the reversedimage in the cases of the images of the left shoulder). Two outputs y₁ ³and y₂ ³ obtained from the third layer (the output layer) are thesignals corresponding to the sensitivity and the contrast during thefinal readout. An i'th unit of a k'th layer is indicated by u_(i) ^(k).The total input into the unit u_(i) ^(k) is indicated by x_(i) ^(k) andthe total output therefrom is indicated by y_(i) ^(k). The weight ofconnection from the unit u_(i) ^(k) to a unit u_(j) ^(k+1) is indicatedby W_(i) ^(k) _(j) ^(k+1). Also, each unit u_(j) ^(k) has the samecharacteristic function, which is expressed as ##EQU1## The input x_(j)^(k) into each unit u_(j) ^(k) and the output y_(j) ^(k) therefrom areexpressed as

    x.sub.j.sup.k Σ.sub.1 W.sub.i.sup.k-1 .sub.j.sup.k •y.sub.i.sup.k-1                                    (4)

    y.sub.j.sup.k =f(x.sub.j.sup.k)                            (5)

Inputs F1, F2, . . . , Fn1 into the units u_(i) ¹, where i=1, 2, . . . ,n1, which units constitute the input layer, are fed into the units u_(i)¹, where i=1, 2, . . . , n1, without being weighted. The n1 number ofsignals F1, F2, . . . , Fn1 are weighted with the weights of connectionW_(i) ^(k) _(j) ^(k+1), and transmitted to the ultimate outputs y₁ ³ andy₂ ³. In this manner, the read-out conditions for the final readout(i.e. the sensitivity and the contrast) are obtained.

How the weights of connection W_(i) ^(k) _(j) ^(k+1) are determined willbe described hereinbelow. First, initial values of the weights ofconnection W_(i) ^(k) _(j) ^(k+1) are given by random numbers. The rangeof the random numbers should preferably be limited such that, even whenthe values of the inputs F1, F2, . . . , Fn1 fluctuate to the largestextent, the outputs y₁ ³ and y₂ ³ may take values falling within apredetermined range or values close to said predetermined range.

Thereafter, preliminary read-out image signals are obtained in themanner described above from a plurality of stimulable phosphor sheetsstoring X-ray images of the right or left shoulder, for which theappropriate read-out conditions for the final readout are known. As forthe X-ray images of the left shoulder, the preliminary read-out imagesignals are reversed. In this manner, the n1 number of inputs F1, F2, .. . , Fn1 are obtained. The n1 number of inputs F1, F2, . . . , Fn1 arefed into the neural network shown in FIG. 3 and the outputs y_(i) ^(k)of the respective units u_(i) ^(k) are monitored.

After the outputs y_(i) ^(k) are obtained, square errors E1 and E2between the ultimate outputs y₁ ³, y₂ ³ and the instructor signals (thesensitivity y₁ ³ and the contrast y₂ ³) representing the read-outconditions for the final readout appropriate for the image arecalculated with the formulas ##EQU2## The weights of connection W_(i)^(k) _(j) ^(k+1) are then corrected such that the square errors E1 andE2 become the smallest. The output y₁ ³ will be described hereinbelow.The descriptions below also apply to the output y₂ ³.

The square error E1 is a function of W_(i) ^(k) _(j) ^(k+1). Therefore,in order for the square error E1 to be minimized, the weights ofconnection W_(i) ^(k) _(j) ^(k+1) are corrected with the formula##EQU3## where η denotes a coefficient, which is referred to as alearning coefficient.

The following formula obtains: ##EQU4## Also, Formula (4) gives

    x.sub.j.sup.k+1 =Σ.sub.1 W.sub.i.sup.k .sub.j.sup.k+1 •y.sub.i.sup.k                                      (4)'

Therefore, Formula (9) gives ##EQU5##

From Formula (6), the following formula obtains: ##EQU6## Formula (11)can be changed with Formula (5) into ##EQU7##

From Formula (3), the following formula obtains:

    f'(x)=f (x) (1-f (x))                                      (13)

Therefore,

    f'(x.sub.1.sup.3)=y.sub.1.sup.3 •(1-y.sub.1.sup.3)   (14)

Setting k=2 in Formula (10) and substituting Formulas (12) and (14) intoFormula (10) yield ##EQU8## Substitution of Formula (15) into Formula(8) yields ##EQU9## The weights of connection W_(i) ² ₁ ³ where i=1, 2,3, are corrected with Formula (16).

Also, the following formula obtains: ##EQU10## Substitution of Formulas(4) and (5) into Formula (17) yields ##EQU11##

Formula (13) gives

    f'(x.sub.j.sup.2)=y.sub.j.sup.2 •(1-y.sub.j.sup.2)   (19)

Substitution of Formulas (12) , (14) , and (19) into Formula (18) yields##EQU12##

Setting k=1 in Formula (10) and substituting Formula (20) into Formula(10) yield ##EQU13## Substitution of Formula (21) into Formula (8) andsetting of k=1 yield ##EQU14## The values of the weights of connectionW_(i) ² ₁ ³, where i=1, 2, . . . , n1, which have been corrected withFormula (16), are substituted into Formula (22). In this manner, theweights of connection W_(i) ¹ _(j) ², where i=1, 2, . . . , n1 and j=1,2, . . . , n2, are corrected.

Theoretically, the weights of connection W_(i) ^(k) _(j) ^(k+1) can beconverged to predetermined values by using Formulas (16) and (22), usinga sufficiently small learning coefficient η and carrying out thelearning operations very many times. However, if a sufficiently smalllearning coefficient η is used, the speed with which the learningoperations are effected will become low. If a very large learningcoefficient η is used, vibration" will occur in the learning operations(i.e. the weights of connection do not converge to predeterminedvalues). Therefore, actually, the vibration is prevented by employing aninertia term, which is expressed in Formula (23), in the calculations ofthe correction amounts for the weights of connection, and the learningcoefficient η is set to a slightly large value. ##EQU15## where αdenotes the coefficient referred to as the inertia term and ΔW_(i) ^(k)_(j) ^(k+1) (t) denotes the correction amount, which is used during thet'th learning operation and which is obtained by subtracting a weight ofconnection W_(i) ^(k) _(j) ^(k+1), which has not been corrected, from aweight of connection W_(i) ^(k) _(j) ^(k+1), which has been corrected.(Such an inertia term is described in, for example, "Learning internalrepresentations by error propagation In Parallel Distributed Processing"by D. E. Rumelhart, G. E. Hinton and R. J. Williams, Volume 1, J. L.McClell and, D. E. Rumelhart and The PDP Research Group, MIT Press,1986b.)

By way of example, the inertia term α is set to 0.9, the learningcoefficient η is set to 0.25, and 200,000 times of corrections (learningoperations) are carried out for each of the weights of correction W_(i)^(k) _(j) ^(k+1). Thereafter, each of the weights of correction W_(i)^(k) _(j) ^(k+1) is fixed at a final value. At the time at which thelearning operations are completed, the two outputs y₁ ³ and y₂ ³represents the appropriate sensitivity and the appropriate contrastduring the final readout.

Therefore, after the learning operations are completed, in order forappropriate read-out conditions for the final readout to be obtained, apreliminary read-out image signal SP representing an X-ray image is fedinto the neural network shown in FIG. 3. The outputs y₁ ³ and y₂ ³obtained from the neural network are utilized as signals representingthe read-out conditions (i.e. the sensitivity and the contrast) for thefinal readout appropriate for the X-ray image. Because the learningoperations have been carried out in the manner described above, thesignals accurately represent the appropriate read-out conditions for thefinal readout.

The number of layers of the neural network is not limited to three.Also, no limitation is imposed on the number of the units of each layer.The number of the units of each layer may be determined in accordancewith the number of the picture elements represented by the preliminaryread-out image signal SP, which is fed into the neural network, theaccuracy, with which the read-out conditions for the final readout areto be obtained, or the like.

The voltage applied to the photomultiplier 21' of the final read-outmeans 100', the amplification factor of the logarithmic amplifier 26',and the like, are controlled in accordance with the read-out conditionsfor the final readout, which have been adjusted by the neural network.The final readout is carried out under the controlled conditions.

In the aforesaid embodiment, before the preliminary read-out imagesignal SP representing an X-ray image of the shoulder is fed into theneural network, pattern matching is effected with respect to thepatterns shown in FIGS. 2A and 2B. A judgment is thereby made as towhether the X-ray image represented by the preliminary read-out imagesignal SP is the standard image (i.e. the image of the right shoulder)or the reversed image (i.e. the image of the left shoulder). In caseswhere the X-ray image represented by the preliminary read-out imagesignal SP is the reversed image (i.e. the image of the left shoulder),the preliminary read-out image signal SP is processed such that theprocessed image signal represents the standard image (i.e. the image ofthe right shoulder). The first apparatus in accordance with the presentinvention is not limited to the processing of images of the shoulder.For example, the first apparatus in accordance with the presentinvention is also applicable when images of the right and left hands,images of the right and left sides of the head or the abdomen, and thelike, are processed.

Also, the first apparatus in accordance with the present invention isnot limited to the processing of images reversed horizontally. Forexample, the first apparatus in accordance with the present invention isalso applicable when an image is to be rotated into a normal orientationin cases where an image signal representing an inclined image isobtained due to oblique setting of a stimulable phosphor sheet duringthe image recording operation, or an image signal representing alaterally inclined image or an inverted image is obtained due to settingof a stimulable phosphor sheet in an incorrect direction during theimage read-out operation. The first apparatus in accordance with thepresent invention is also applicable when images having different scalesof enlargement (or reduction), which are obtained from, for example, adirect image recording operation and fluorography, are to be corrected.The first apparatus in accordance with the present invention is furtherapplicable when position adjustment is to be carried out such that anobject image region may be located at the center area of an image incases where the object image pattern was recorded at a peripheral partof the image. Additionally, the first apparatus in accordance with thepresent invention is applicable when a combination of the aforesaidprocesses is to be carried out.

In the aforesaid embodiment, the preliminary readout means 100 and thefinal read-out means 100' are separate from each other. Alternatively,because the configurations of the preliminary read-out means 100 and thefinal read-out means 100' are approximately identical to each other, asingle read-out means may be utilized for performing both thepreliminary readout and the final readout. In this case, after beingsubjected to the preliminary readout, the stimulable phosphor sheet 11may be moved back to the position at which image readout is started.Thereafter, the final readout may be carried out.

In cases where a single read-out means is utilized to perform both thepreliminary readout and the final readout, it is necessary to change theintensity of the light beam used in the preliminary readout and thefinal readout. For this purpose, various methods may be employed asdescribed above, for example, a laser beam source or the like may changethe intensity of the light beam.

In the aforesaid embodiment, the read-out conditions for the finalreadout are adjusted by the computer system 40. Alternatively,predetermined read-out conditions may be used when the final readout iscarried out regardless of the characteristics of the preliminaryread-out image signal SP. On the basis of the preliminary read-out imagesignal SP, the computer system 40 may adjust the image processingconditions to be used in carrying out image processing of the imagesignal SQ. The computer system 40 may also adjust both the read-outconditions and the image processing conditions.

The aforesaid embodiment is applied to the radiation image read-outapparatus wherein the preliminary readout is carried out. However, thefirst apparatus in accordance with the present invention is alsoapplicable to radiation image read-out apparatuses wherein nopreliminary read-out operations are carried out, and only the aforesaidfinal read-out operations are carried out. In these cases, an imagesignal is obtained by use of predetermined read-out conditions. Based onthe image signal, image processing conditions are calculated by thecomputer system 40. The image signal is processed under the calculatedimage processing conditions.

An embodiment of the first method, i.e. the method for adjustingread-out conditions and/or image processing conditions for a radiationimage, in accordance with the present invention will be describedhereinbelow. In this embodiment, a stimulable phosphor sheet is used,and an X-ray image having a pattern of the shoulder joint of a humanbody as a region of interest is processed.

FIGS. 6A and 6B are explanatory views showing X-ray images of theshoulder joint, which images have been stored on stimulable phosphorsheets 11 in the X-ray image recording apparatus shown in FIG. 4 in themanner described above.

In this embodiment, in the computer system 40 shown in FIG. 5, theread-out conditions for the final readout are adjusted on the basis ofthe preliminary read-out image signal SP in the manner described below.

By using the X-ray image read-out apparatus shown in FIG. 5, preliminaryread-out image signals are obtained in the manner described above from aplurality of stimulable phosphor sheets storing X-ray images having ashoulder joint pattern 9 as shown in FIGS. 6A and 6B, for which theappropriate read-out conditions for the final readout are known. In thismanner, the n1 number of inputs F1, F2, . . . , Fn1 are obtained. Inthis embodiment, under the appropriate read-out conditions for the finalreadout, an image signal is obtained which represents an X-ray imagesuch that the pattern of the shoulder joint 9 may have an appropriateimage density.

The n1 number of inputs F1, F2, . . . , Fn1 are fed into the neuralnetwork shown in FIG. 3, and the learning operations of the neuralnetwork are carried out in the same manner as that described above. Atthe time at which the learning operations are completed the two outputsy₁ ³ and y₂ ³ represents the appropriate sensitivity and the appropriatecontrast during the final readout (i.e. such that the pattern of theshoulder joint 9 may have an appropriate image density in a reproducedX-ray image).

Therefore, after the learning operations are completed, in order forappropriate read-out conditions for the final readout to be obtained, apreliminary read-out image signal SP representing an X-ray image is fedinto the neural network shown in FIG. 3. The outputs y₁ ³ and y₂ ³obtained from the neural network are utilized as signals representingthe read-out conditions (i.e. the sensitivity and the contrast) for thefinal readout appropriate for the X-ray image. Because the learningoperations have been carried out in the manner described above, thesignals accurately represent the appropriate read-out conditions for thefinal readout.

The voltage applied to the photomultiplier 21' of the final read-outmeans 100', the amplification factor of the logarithmic amplifier 26',and the like, are controlled in accordance with the read-out conditionsfor the final readout, which have been adjusted by the neural network.The final readout is carried out under the controlled conditions.

In the aforesaid embodiment, the read-out conditions for the finalreadout are adjusted by the computer system 40. Alternatively,predetermined read-out conditions may be used when the final readout iscarried out regardless of the characteristics of the preliminaryread-out image signal SP. On the basis of the preliminary read-out imagesignal SP, the computer system 40 may adjust the image processingconditions to be used in carrying out image processing of the imagesignal SQ. The computer system 40 may also adjust both the read-outconditions and the image processing conditions.

As illustrated in FIG. 8, in an embodiment of the second method inaccordance with the present invention, the preliminary read-out imagesignal SP obtained in the preliminary read-out means 100 is fed into aneural network constituted of a computer system 200. An image pattern inthe predetermined region of interest is determined, and the preliminaryread-out image signal SP' is sampled which represents only the imagepattern corresponding to the region of interest. The sampled preliminaryread-out image signal SP' is fed into a probability density functionanalyzing means 201, which adjusts the read-out conditions for the finalreadout on the basis of the results of an analysis of the probabilitydensity function of the preliminary read-out image signal SP'. In suchcases, the read-out conditions for the final readout are adjusted onlyfor the image pattern corresponding to the region of interest.Therefore, the read-out conditions for the final readout, which havethus been adjusted, are always optimum for the image patterncorresponding to the region of interest.

Information C representing the read-out conditions for the final readoutis then fed into the final read-out means 100', and the read-outconditions for the final readout are adjusted in accordance with theinformation C. In this manner, a reproduced visible image can beobtained in which the image pattern corresponding to the region ofinterest has an appropriate image density.

Techniques for analyzing probability density functions are described indetail in, for example, Japanese Unexamined Patent Publication Nos.60(1985)-185944 and 61(1986)-280163. In this embodiment, such knowntechniques for analyzing probability density functions may be employed.

In cases where the image pattern corresponding to a predetermined regionof interest is determined by the neural network, appropriate imageprocessing conditions can be adjusted on the basis of the results of ananalysis of the probability density function.

In lieu of the probability density function analyzing means 201, aneural network may be employed to adjust the read-out conditions for thefinal readout and/or the image processing conditions.

The aforesaid embodiments of the first and second methods in accordancewith the present invention are applied to the radiation image read-outmethod wherein the preliminary readout is carried out. However, thefirst and second methods in accordance with the present invention arealso applicable to radiation image read-out methods wherein nopreliminary read-out operations are carried out, and only the aforesaidfinal read-out operations are carried out. FIG. 9 shows such anembodiment.

In this embodiment, an image signal SQ is obtained by use ofpredetermined read-out conditions in the final read-out means 100'.Based on the image signal SQ, appropriate image processing conditionsare calculated by a computer system 210 constituting a neural network.Also, in such cases, the learning operations of the neural network arecarried out by utilizing radiation images having image patterns of aspecific region of interest. In this manner, the image processingconditions can be obtained which are optimum for the image pattern ofthe region of interest.

Information D representing the optimum image processing conditions,which have thus been adjusted, is fed into an image processing unit 211.In the image processing unit 211, image processing, such as gradationprocessing, is carried out on the image signal SQ under the optimumimage processing conditions.

Also, as illustrated in FIG. 10, the image signal SQ obtained in thefinal read-out means 100' may be fed into a neural network constitutedof a computer system 220. An image pattern in the predetermined regionof interest is determined, and the image signal SQ' is sampled whichrepresents only the image pattern corresponding to the region ofinterest. The sampled image signal SQ' is fed into a probability densityfunction analyzing means 221, which adjusts the image processingconditions on the basis of the results of an analysis of the probabilitydensity function of the image signal SQ'. In such cases, the imageprocessing conditions are adjusted only for the image patterncorresponding to the region of interest. Therefore, the image processingconditions, which have thus been adjusted, are always optimum for theimage pattern corresponding to the region of interest.

Information E representing the image processing conditions is then fedinto the image processing unit 211, and the image processing conditionsare adjusted in accordance with the information E. In this manner, areproduced visible image can be obtained in which the image patterncorresponding to the region of interest has an appropriate imagedensity.

Also, in such cases, in lieu of the probability density functionanalyzing means 221, a neural network may be utilized to adjust theimage processing conditions.

An embodiment of the third apparatus, i.e. the apparatus for adjustingread-out conditions and/or image processing conditions for a radiationimage, in accordance with the present invention will be describedhereinbelow. In this embodiment, a stimulable phosphor sheet is used,and an X-ray image having a pattern of the shoulder joint of a humanbody as a region of interest is processed.

This embodiment is incorporated in the computer system 40 shown in FIG.5.

The preliminary read-out image signal SP is obtained by reading theX-ray image shown in FIG. 6A or FIG. 6B in the X-ray image read-outapparatus of FIG. 5 in the same manner as that described above. Thepreliminary read-out image signal SP is fed into the computer system 40.

In the computer system 40, when necessary, a subdivision pattern and theshape and location of an irradiation field are determined from thepreliminary read-out image signal SP. Thereafter, the probabilitydensity function of the preliminary read-out image signal SP is created.The read-out conditions for the final readout, i.e. the sensitivity Skand the latitude Gp during the final readout, are determined by a neuralnetwork on the basis of the results of an analysis of the probabilitydensity function. By way of example, the voltage applied to aphotomultiplier 21' and the amplification factor of a logarithmicamplifier 26' are controlled in accordance with the sensitivity Sk andthe latitude Gp.

In the computer system 40, the read-out conditions for the final readoutand/or the image processing conditions are temporarily determined on thebasis of the results of an analysis of the probability density functionof the preliminary read-out image signal SP. Also, the conditions, whichhave been temporarily determined, are corrected by a neural network. Inthis manner, the read-out conditions for the final readout and/or theimage processing conditions are adjusted finally.

FIG. 11 shows such processes carried out in the computer system 40.

Specifically, as shown in FIG. 11, the embodiment of the third apparatusin accordance with the present invention is provided with a probabilitydensity function analyzing means 51, which receives an image signal 50,temporarily determines the read-out conditions for the final readout onthe basis of the results of an analysis of the probability densityfunction of the image signal 50, and feeds out information representingthe temporarily determined conditions. This embodiment is also providedwith a neural network 52, which receives the image signal 50, determinescorrection values ΔSmax and ΔSmin to be used in correcting the read-outconditions for the final readout, Smax and Smin, which have beentemporarily determined by the probability density function analyzingmeans 51, and feeds out information representing the correction values.This embodiment is further provided with an addition means 53, whichadds the correction values ΔSmax and ΔSmin having been determined by theneural network 52 to the read-out conditions for the final readout, Smaxand Smin, having been temporarily determined by the probability densityfunction analyzing means 51, and feeds out information representing theread-out conditions for the final readout, Smax' and Smin'(Smax'=Smax+ΔSmax, Smin'=Smin+ΔSmin). The conditions are temporarilydetermined by the probability density function analyzing means 51, andare corrected by the neural network 52. In this manner, the read-outconditions for the final readout is adjusted finally.

In the aforesaid embodiment of the third apparatus in accordance withthe present invention, Smax' and Smin' corresponding to the maximumvalue and the minimum value of the image signal are employed as theread-out conditions for the final readout. Alternatively, other values,which correspond to the sensitivity and the scale factor, may beemployed as the read-out conditions for the final readout.

An embodiment of the fifth apparatus in accordance with the presentinvention will be described hereinbelow. This embodiment is incorporatedin the computer system 40 shown in FIG. 5. How the preliminary read-outimage signal SP is processed in the computer system 40 will be describedbelow.

In this embodiment, in the computer system 40, the read-out conditionsfor the final readout and/or the image processing conditions aretemporarily determined on the basis of the results of an analysis of theprobability density function of the preliminary read-out image signalSP. Also, the read-out conditions for the final readout and/or the imageprocessing conditions are finally adjusted by a neural network on thebasis of the temporarily determined conditions and the preliminaryread-out image signal SP.

FIG. 12 shows such processes carried out in the computer system 40.

Specifically, as shown in FIG. 12, the embodiment of the fifth apparatusin accordance with the present invention is provided with a probabilitydensity function analyzing means 61, which receives an image signal 60,temporarily determines the read-out conditions for the final readout onthe basis of the results of an analysis of the probability densityfunction of the image signal 60, and feeds out information representingthe temporarily determined conditions. This embodiment is also providedwith a neural network 62, which receives the image signal 60 and theread-out conditions for the final readout, Sk' and GP', having beentemporarily determined by the probability density function analyzingmeans 61 and feeds out information representing the read-out conditionsfor the final readout, Sk and Gp, having been adjusted finally. Theconditions are temporarily determined by the probability densityfunction analyzing means 61, and the read-out conditions for the finalreadout is adjusted finally by the neural network 62 on the basis of theimage signal 60 and the temporarily determined conditions, Sk' and Gp'.

In this embodiment of the fifth apparatus in accordance with the presentinvention, Sk and Gp corresponding to the sensitivity and the contrastof the image signal are employed as the read-out conditions for thefinal readout. Alternatively, other values, which correspond to thesensitivity and the scale factor, may be employed as the read-outconditions for the final readout. By way of example, as in the aforesaidembodiment of the third apparatus in accordance with the presentinvention, the values corresponding to the maximum value and the minimumvalue of the image signal may be employed as the read-out conditions forthe final readout.

How the neural network employed in the aforesaid embodiment of the thirdapparatus in accordance with the present invention works will bedescribed hereinbelow.

In this embodiment, signals F1, F2, . . . , Fn1 fed into the first layer(the input layer) of the neural network shown in FIG. 3 are the imagesignal components of the preliminary read-out image signal SPrepresenting the picture elements in the X-ray image. Two outputs y₁ ³and y₂ ³ obtained from the third layer (the output layer) are thesignals corresponding to the correction values to be used in correctingthe results of the analysis of the probability density function.

By using the X-ray image read-out apparatus shown in FIG. 5, preliminaryread-out image signals are obtained in the manner described above from aplurality of stimulable phosphor sheets storing X-ray images having ashoulder joint pattern 9 as shown in FIGS. 6A and 6B, for which theappropriate read-out conditions for the final readout are known. In thismanner, the n1 number of inputs F1, F2, . . . , Fn1 are obtained. Inthis embodiment, under the appropriate read-out conditions for the finalreadout, an image signal is obtained which represents an X-ray imagesuch that the pattern of the shoulder joint 9 may have an appropriateimage density. Outputs are obtained which represent the correctionvalues to be used in correcting different results of an analysis of theprobability density function.

The n1 number of inputs F1, F2, . . . , Fn1 are fed into the neuralnetwork shown in FIG. 3, and the learning operations of the neuralnetwork are carried out in the same manner as that described above. Inthis embodiment, the instructor signals y₁ ³ and y₂ ³ represent thecorrection values, ΔSmax and ΔSmin, which are appropriate for the image.At the time at which the learning operations are completed, the twooutputs y₁ ³ and y₂ ³ represents the appropriate correction values to beused in correcting the sensitivity and the contrast during the finalreadout (i.e. such that the pattern of the shoulder joint 9 may have anappropriate image density in a reproduced X-ray image). In cases whereno correction need be carried out, a correction value of 0 is fed out.

Therefore, after the learning operations are completed, in order forappropriate read-out conditions for the final readout to be obtained, apreliminary read-out image signal SP representing an X-ray image is fedinto the neural network shown in FIG. 3. The outputs y₁ ³ and y₂ ³obtained from the neural network are utilized as signals representingthe correction values to be used in correcting the read-out conditionsfor the final readout, which have been temporarily determined on thebasis of the results of an analysis of the probability density function.Because the learning operations have been carried out in the mannerdescribed above, the signals represent the correction values foraccurately correcting the read-out conditions for the final readout.

The correction values, which have thus been obtained, are added to theoutputs resulting from the analysis of the probability density function.In this manner, the optimum read-out conditions for the final readoutare obtained.

The voltage applied to the photomultiplier 21' of the final read-outmeans 100', the amplification factor of the logarithmic amplifier 26',and the like, are controlled in accordance with the read-out conditionsfor the final readout, which have been corrected by the neural network.The final readout is carried out under the controlled conditions.

In the aforesaid embodiment of the fifth apparatus in accordance withthe present invention, the neural network works in the same manner asthat in the neural network employed in the aforesaid embodiment of thethird apparatus in accordance with the present invention. The imagesignal components of the preliminary read-out image signal SP, whichrepresent the picture elements in the X-ray image, and the conditions(the sensitivity and the contrast), which have been temporarilydetermined from the analysis of the probability density function, arefed into the first layer (the input layer) of the neural network. Twooutputs y₁ ³ and y₂ ³ obtained from the third layer (the output layer)are the signals corresponding to the final read-out conditions for thefinal readout (i.e. the sensitivity and the contrast).

At the time at which the learning operations of the neural networkemployed in the embodiment of the fifth apparatus in accordance with thepresent invention are completed the two outputs y₁ ³ and y₂ ³ representsthe appropriate sensitivity and the appropriate contrast during thefinal readout (i.e. such that the pattern of the shoulder joint 9 mayhave an appropriate image density in a reproduced X-ray image).

In the manner described above, in the embodiment of the fifth apparatusin accordance with the present invention, the appropriate read-outconditions for the final readout are adjusted by the neural network. Thevoltage applied to the photomultiplier 21' of the final read-out means100', the amplification factor of the logarithmic amplifier 26', and thelike, are controlled in accordance with the read-out conditions for thefinal readout, which have been adjusted by the neural network. The finalreadout is carried out under the controlled conditions.

In the aforesaid embodiments of the third and fifth apparatuses inaccordance with the present invention, the read-out conditions for thefinal readout are adjusted by the computer system 40. Alternatively, theimage processing conditions, under which the image signal SQ is to beimage processed, may be determined by the computer system 40.

Specifically, the method for adjusting the read-out conditions for thefinal readout by the computer system 40 provided with probabilitydensity function analyzing means the neural network may be applied whenthe image processing conditions are to be determined. In such cases,predetermined read-out conditions may be used when the final readout iscarried out regardless of the characteristics of the preliminaryread-out image signal SP. On the basis of the preliminary read-out imagesignal SP, the computer system 40 may adjust the image processingconditions to be used in carrying out image processing of the imagesignal SQ. The computer system 40 may also adjust both the read-outconditions and the image processing conditions.

The aforesaid embodiments of the third and fifth apparatuses inaccordance with the present invention are applied to the radiation imageread-out method wherein the preliminary readout is carried out. However,the third and fifth apparatuses in accordance with the present inventionare also applicable to radiation image read-out methods wherein nopreliminary read-out operations are carried out, and only the aforesaidfinal read-out operations are carried out. In such cases, as anembodiment of the fourth apparatus in accordance with the presentinvention, the image processing conditions are determined on the basisof the results of an analysis of the probability density function of animage signal, which has been detected with an appropriate method. Theimage processing conditions are then corrected by a computer systemconstituting a neural network. In an embodiment of the sixth apparatusin accordance with the present invention, the image processingconditions are determined on the basis of the results of an analysis ofthe probability density function of an image signal, which has beendetected with an appropriate method. Thereafter, on the basis of theconditions thus determined and the image signal, a neural networkadjusts appropriate image processing conditions.

In the embodiments of the fourth and sixth apparatuses in accordancewith the present invention, an image stored on the stimulable phosphorsheet is read out. The fourth and sixth apparatuses in accordance withthe present invention are also applicable when image signals aredetected from images, such as medical images, which have been recordedon conventional X-ray film, or the like.

Information representing the optimum image processing conditions, whichhave thus been adjusted, is fed into an image processing unit. In theimage processing unit, image processing, such as gradation processing,is carried out on the image signal under the optimum image processingconditions.

An embodiment of the seventh apparatus, i.e. the radiation imageread-out apparatus, in accordance with the present invention will bedescribed hereinbelow.

FIGS. 13A and 13B are explanatory views showing X-ray images stored onstimulable phosphor sheets.

The X-ray images shown in FIGS. 13A and 13B are of the right shoulder.However, in the X-ray image of FIG. 13A, patterns of vertebral bodies 10are included, and the area of a lung field pattern 76 is large.

On the other hand, in the X-ray image of FIG. 13B, no patterns ofvertebral bodies 10 are included, and the area of a lung field pattern76 is small. In general, images of various portions of an object, suchas the head, the chest, and the abdomen, are stored on stimulablephosphor sheets.

This embodiment is constituted in the same manner as that shown in FIG.5. Examples of the latitude operating means and the sensitivityoperating means of the seventh apparatus in accordance with the presentinvention are incorporated in the computer system 40.

FIG. 14 shows how the read-out conditions for the final readout areadjusted in the computer system 40.

When the preliminary read-out image signal SP is fed into the computersystem 40, its probability density function is analyzed, and thelatitude Gp is determined from the results of the analysis. Thepreliminary read-out image signal SP is also fed into a neural network,which determines the sensitivity Sk.

The computer system 40 stores information concerning algorithms, whichanalyze probability density functions and determine latitudes Gp varyingin accordance with characteristics of an image, such as portion of theobject the image of which was recorded (e.g. the head, the neck, thechest, or the abdomen), the orientation in which the object was placedwhen the image of the object was recorded (e.g. a front image, a rightside image, or a left side image), and the mean value of the imagesignal (i.e. the mean value of the amount of energy stored on thestimulable phosphor sheet). The computer system 40 also storesinformation concerning the neural network (or the coefficientsrepresenting the weight of connections of neurons constituting theneural network), which determines the sensitivity Sk suitable for thecharacteristics of the image. When the preliminary read-out image signalSP is fed into the computer system, the information, which representsthe corresponding probability density function analyzing algorithm, andthe information, which represents the corresponding neural network, areread from the memory of the computer system. The probability densityfunction analyzing algorithm and the neural network determine thelatitude Gp and the sensitivity Sk as the read-out conditions for thefinal readout. The voltage applied to the photomultiplier 21', theamplification factor of the logarithmic amplifier 26', and the like, arecontrolled in accordance with the sensitivity Sk and the latitude Gp.

How the algorithm works to determine the latitude Gp by analyzing aprobability density function will be described hereinbelow.

FIG. 15 shows a probability density function of the preliminary read-outimage signal SP detected from the X-ray image shown in FIG. 13A or FIG.13B. Approximately the same probability density functions are obtainedwhen the patterns of the vertebral bodies are included and the area ofthe lung field pattern is large in the X-ray image of FIG. 13A and whenno patterns of the vertebral bodies are included and the area of thelung field pattern is small in the X-ray image of FIG. 13B. Therefore, asingle probability density function is shown in FIG. 15.

With reference to FIG. 15, the values of the preliminary read-out imagesignal SP, which were obtained by detecting the light emitted by astimulable phosphor sheet during a preliminary readout and areproportional to the amount of light emitted, are plotted on thehorizontal axis, which has a logarithmic scale. The relative frequencyof occurrence of the values of the preliminary read-out image signal SPis plotted on the vertical axis at the upper part of the graph, and thevalues of the image signal obtained during the final readout are plottedon a logarithmic scale on the vertical axis at the lower part of thegraph. The probability density function of the preliminary read-outimage signal SP is composed of projecting parts A and B. The projectingpart B corresponds to a background region 6 shown in FIGS. 13A and 13B,upon which the X-rays 3 impinged directly without passing through theobject 4. It is unnecessary for the image information corresponding tothe projecting part B to be reproduced. The projecting part Acorresponds to the object image region, upon which the X-rays 3 havingpassed through the object 4 impinged. The region which it is necessaryto reproduce varies for the image shown in FIG. 13A and the image shownin FIG. 13B. The region 78 of the probability density functioncorresponds to the region in the image shown in FIG. 13A, which it isnecessary to reproduce. The region 79 of the probability densityfunction corresponds to the region in the image shown in FIG. 13B, whichit is necessary to reproduce. In order for the read-out conditions (thesensitivity Sk and the latitude Gp) for the final readout to bedetermined which are suitable for the X-ray image shown in FIG. 13A, theread-out conditions for the final readout should be determined suchthat, during the final readout, the minimum value SP1 of the preliminaryread-out image signal SP falling within the region 78 may be detected asthe minimum image signal value SQmin, and the maximum value SP2 of thepreliminary read-out image signal SP falling within the region 78 may bedetected as the maximum image signal value SQmax. Specifically, theread-out conditions for the final readout should be determined suchthat, during the final readout, the image information represented byvalues of the emitted light signal falling within the range of SP1 toSP2 is detected as an image signal having values lying on the straightline G1. In order for the read-out conditions for the final readout tobe determined which are suitable for the X-ray image shown in FIG. 13B,the read-out conditions for the final readout should be determined suchthat, during the final readout, the image information represented byvalues of the emitted light signal falling within the range of theminimum value to the maximum value of the preliminary read-out imagesignal SP corresponding to the projecting part 79 is detected as animage signal having values lying on the straight line G2. However, itcannot be discriminated from the probability density function whetherthe X-ray image was recorded as shown in FIG. 13A or FIG. 13B.Therefore, it cannot be determined whether the read-out conditions forthe final readout, which corresponds to the straight line G1, is to beset or the read-out conditions for the final readout, which correspondsto the straight line G2, is to be set.

Therefore, heretofore, by way of example, the values of the probabilitydensity function are compared to a threshold value T, starting with thevalue of the function at the minimum value SP1 of the preliminaryread-out image signal SP and working along the direction of increase ofthe image signal values, i.e. along the chained line. When theprobability density function crosses through the threshold value T, acalculation is made to find out whether the function is rising orfalling. In this manner, a first rising point "a" and a second fallingpoint "b" are found. The read-out conditions for the final readout areset so that, during the final readout, the minimum value and the maximumvalue of the range D sandwiched between the points "a" and "b" aredetected as the minimum value SQmin and the maximum value SQmax of theimage signal SQ. Specifically, the read-out conditions for the finalreadout are set so that, during the final readout, the image informationrepresented by values of the emitted light signal falling within therange D is detected as an image signal having values lying on thestraight line G3. In such cases, problems occur in that, for example,the latitude is wide, and therefore an image is obtained which has a lowdensity resolution. The sensitivity Sk corresponds to the positions ofthe straight lines G1, G2, and G3 with respect to the horizontaldirection (the sensitivity Sk is high for a left straight line). Thelatitude Gp corresponds to the inclination of the straight line (thelatitude Gp is narrow when the angle of inclination of the straight lineis large). When the straight lines G1 and G2 are compared with eachother, the sensitivity Sk (i.e. the position with respect to thehorizontal direction) differs markedly, and the latitude Gp (i.e. theangle of inclination) is nearly the same. It has been known that, whenthe region of interest is the same, even if the object shifts during theimage recording operations, the latitude Gp is kept approximately thesame. Therefore, in this embodiment, a predetermined proportion (e.g.1/3) with respect to the range D sandwiched between the points "a" and"b", which have been found in the manner described above, is taken asthe latitude Gp. The sensitivity Sk is determined by the neural networkshown in FIG. 3.

In this embodiment, signals F1, F2, . . . , Fn1 fed into the first layer(the input layer) of the neural network shown in FIG. 3 are the imagesignal components of the preliminary read-out image signal SPrepresenting the picture elements in the X-ray image, which image signalcomponents have been thinned out . The output y₁ ³ obtained from thethird layer (the output layer) are the signal corresponding to thesensitivity Sk during the final readout.

By using the X-ray image read-out apparatus shown in FIG. 5, preliminaryread-out image signals are obtained in the manner described above from aplurality of stimulable phosphor sheets storing X-ray images, for whichthe appropriate read-out conditions (sensitivity Sk) for the finalreadout are known. The preliminary read-out image signal SP is thenthinned out. In this manner, the n1 number of inputs F1, F2, . . . , Fn1are obtained. The n1 number of inputs F1, F2, . . . , Fn1 are fed intothe neural network shown in FIG. 3, and the learning operations of theneural network are carried out in the same manner as that describedabove. In this embodiment, the instructor signal y₁ ³ represents thesensitivity, which is appropriate for the image. By carrying out thelearning operations, the weight of connection W_(i) ^(k) _(j) ^(k+1) isfixed at a final value. In this case, after the system provided with thedetermining apparatus for determining the read-out conditions for thefinal readout by using the neural network is located at the user, thelearning operations are continued. Therefore, the final value means thefinal value at the original operation starting stage at the user. Whenthe learning operations are finished, the output y₁ ^(k) represents anapproximately appropriate sensitivity Sk during the final readout.

The neural network whose learning operations have been finished isprepared in the manner described above. By using the neural network,different values of the sensitivity Sk are determined for the X-rayimages shown in FIGS. 13A and 13B. The final readout is carried out byusing the sensitivity Sk, which has been determined by the neuralnetwork, and the latitude Gp, which has been determined on the basis ofthe results of an analysis of the probability density function.

In the aforesaid embodiment, the read-out conditions for the finalreadout are adjusted by the probability density function analyzing meansand the neural network. Alternatively, predetermined read-out conditionsmay be used when the final readout is carried out regardless of thecharacteristics of the preliminary read-out image signal SP. Theprobability density function analyzing means and the neural network mayadjust the image processing conditions to be used in carrying out imageprocessing of the image signal SQ. The probability density functionanalyzing means and the neural network may also adjust both the read-outconditions and the image processing conditions.

FIG. 16 shows an embodiment of the eighth apparatus in accordance withthe present invention. In this embodiment, a stimulable phosphor sheetis used, but no preliminary readout is carried out.

In this embodiment, the read-out means 100' is constituted in the samemanner as in the final read-out means 100' shown in FIG. 5. In FIG. 16,similar elements are numbered with the same reference numerals withrespect to FIG. 5.

The image signal SQ obtained from the A/D converter 27' is fed into acomputer system 40'. In the computer system 40', the image processingconditions, under which the image signal SQ is to be image processed,are determined in the same manner as that in the aforesaid embodiment ofthe seventh apparatus in accordance with the present invention such thata visible image can be obtained which has an appropriate density and anappropriate contrast. As the image processing conditions, the latitudeis determined by the probability density function analyzing means, andthe sensitivity is determined by the neural network. The image signalobtained from the image processing is fed into an image reproducingapparatus (not shown), which reproduces a hard copy of the radiationimage from the image signal.

In the aforesaid embodiments of the seventh and eighth apparatuses inaccordance with the present invention, the stimulable phosphor sheet isused. The seventh and eighth apparatuses in accordance with the presentinvention are also applicable when other recording media, such as X-rayfilm, are used.

An embodiment of the ninth method, i.e. the method for adjustingread-out conditions and/or image processing conditions for a radiationimage, in accordance with the present invention will be describedhereinbelow.

FIG. 17 shows the embodiment of the ninth method in accordance with thepresent invention. The ninth apparatus for carrying out the ninth methodin accordance with the present invention is provided with a means 302for determining the position of the center point of the pattern of theobject in an radiation image on the basis of an image signal 301representing the radiation image of the object. The ninth apparatus isalso provided with a neural network 303, which receives the output ofthe object center position determining means 302 and the image signal301 and adjusts the read-out conditions for the final readout and/or theimage processing conditions on by taking the position of the centerpoint of the pattern of the object into consideration.

When the image signal 301 is fed into the neural network 303 and theinformation, which represents the read-out conditions for the finalreadout and/or the image processing conditions, is fed out from theneural network 303, the information representing the position of thecenter point of the pattern of the object in the image is fed into theneural network 303. Therefore, in the neural network 303, the read-outconditions for the final readout and/or the image processing conditionscan be adjusted on the basis of the image signal by considering theposition of the center point of the pattern of the object.

The embodiment of the ninth apparatus is incorporated in the computersystem 40 shown in FIG. 5.

In the computer system 40, when necessary, a subdivision pattern and theshape and location of an irradiation field are determined from thepreliminary read-out image signal SP. Thereafter, the read-outconditions for the final readout, i.e. the sensitivity Sk and thelatitude Gp during the final readout, are determined by the neuralnetwork on the basis of the preliminary read-out image signal SP. By wayof example, the voltage applied to a photomultiplier 21' and theamplification factor of a logarithmic amplifier 26' are controlled inaccordance with the sensitivity Sk and the latitude Gp. At this time,the information, which represents the position of the center point ofthe pattern of the object, is fed into the neural network together withthe preliminary read-out image signal SP. The neural network adjusts thesensitivity Sk and the latitude Gp on the basis of the preliminaryread-out image signal SP by considering the position of the center pointof the pattern of the object.

In the computer system 40, the preliminary read-out image signal SP isfed into the neural network 303 and the object center positiondetermining means 302. The neural network 303 adjusts the read-outconditions for the final readout on the basis of the preliminaryread-out image signal SP by considering the position of the center pointof the pattern of the object.

In order for the position of the center point of the pattern of theobject to be determined, the method disclosed in Japanese UnexaminedPatent Publication No. 2(1990)-28782 may be employed. The disclosedmethod for determining an image point in an object image comprises thesteps of:

i) on the basis of an image signal comprising image signal componentsrepresenting image information at respective picture elements on arecording medium (such as a stimulable phosphor sheet or photographicfilm) on which a radiation image including an object image has beenrecorded, weighting the respective picture elements with image signalvalues corresponding to the respective picture elements or with thereciprocals of said image signal values, thereby to find the center ofgravity on said recording medium, and

ii) determining a position, at which said center of gravity is located,as the image point in said object image.

Alternatively, a method for determining an image point in an objectimage may be employed, which comprises the steps of:

i) on the basis of an image signal comprising image signal componentsrepresenting image information at respective picture elements on arecording medium (such as a stimulable phosphor sheet or photographicfilm) on which a radiation image including an object image has beenrecorded, arraying image signal values corresponding to the respectivepicture elements or the reciprocals of said image signal values so thatthe positions of said image signal values or the positions of saidreciprocals of said image signal values coincide with the positions ofthe corresponding picture elements,

ii) cumulating said image signal values or said reciprocals of saidimage signal values along each of two different directions on saidrecording medium, and plotting the resulting cumulative values of saidimage signal values or the resulting cumulative values of saidreciprocals of said image signal values along each of said two differentdirections, thereby to find the distributions of the cumulative valuesalong said two different directions,

iii) detecting a coordinate point along each of said two differentdirections, at which point the cumulative value is approximately onehalf of the maximum cumulative value, from each of said distributions ofthe cumulative values, and

iv) determining a position on said recording medium, which position isdefined by the coordinate points detected along said two differentdirections, as the image point in said object image.

In the two methods for determining an image point in an object image,whether to use the image signal values or the reciprocals of the imagesignal values may be determined in the manner described below. Aftersaid image signal is detected, calculations are made based on said imagesignal to find a first representative value which is representative ofthe image signal values corresponding to the peripheral portion of saidrecording medium, and a second representative value which isrepresentative of the image signal values corresponding to the overallarea of said recording medium or corresponding to approximately thecenter portion of said recording medium. Said first representative valueand said second representative value are compared with each other, andwhether to use the image signal values or the reciprocals of the imagesignal values is selected in accordance with the results of thecomparison.

How the learning operations of the neural network are repeated andappropriate read-out conditions for the final readout are adjustedthereby will be described hereinbelow. The neural network is constitutedas shown in FIG. 3. Signals F1, F2, . . . , Fn1 fed into the first layer(the input layer) are the image signal components of the preliminaryread-out image signal SP representing the picture elements in the X-rayimage. Two outputs y₁ ³ and y₂ ³ obtained from the third layer (theoutput layer) are the signals corresponding to the sensitivity and thecontrast during the final readout.

Preliminary read-out image signals are obtained in the manner describedabove from a plurality of stimulable phosphor sheets storing X-rayimages, for which the appropriate read-out conditions for the finalreadout are known. In this manner, the n1 number of inputs F1, F2, . . ., Fn1 are obtained. The n1 number of inputs F1, F2, . . . , Fn1 are fedinto the neural network shown in FIG. 3, and the learning operations ofthe neural network are repeated in the same manner as that describedabove. The instructor signals representing the read-out conditions forthe final readout appropriate for the image represent the sensitivity y₁³ and the contrast y₂ ³. At the time at which the learning operationsare completed the two outputs y₁ ³ and y₂ ³ represents the appropriatesensitivity and the appropriate contrast during the final readout.

Therefore, after the learning operations are completed, in order forappropriate read-out conditions for the final readout to be obtained, apreliminary read-out image signal SP representing an X-ray image is fedinto the neural network shown in FIG. 3. The outputs y₁ ³ and y₂ ³obtained from the neural network are utilized as signals representingthe read-out conditions (i.e. the sensitivity and the contrast) for thefinal readout appropriate for the X-ray image. Because the learningoperations have been carried out in the manner described above, thesignals accurately represent the appropriate read-out conditions for thefinal readout.

In cases where the read-out conditions for the final readout and/or theimage processing conditions are determined by using only the neuralnetwork, judgments are made based on the image signal representing thewhole image. Therefore, if the position of the object image region inthe image (shown in FIG. 18B) shifts largely from the standard positionin the image shown in FIG. 18A, when judgments are made, the same weightas that for the image signal components corresponding to the objectimage region is assigned to the image signal components corresponding tothe background region, and therefore errors will occur in making thejudgments. Also, because the image signal is directly fed into theneural network and the learning operations of the neural network arerepeated, if image signals representing many images in which the objectimage regions shift are fed into the neural network, a very long periodis required for the learning operations to be carried out.

Therefore, in the ninth method in accordance with the present invention,when the image signal is fed into the neural network and the read-outconditions for the final readout are determined by the neural network,the information representing the position of the center point of thepattern of the object in the image is fed into the neural network. Inthe neural network, the whole image signal is shifted in accordance withthe shift of the center point of the pattern of the object such that theposition of the center point of the pattern of the object, which wasemployed during the learning operations, and the position of the centerpoint of the pattern of the object in the image represented by the imagesignal fed into the neural network may coincide with each other.Thereafter, the read-out conditions for the final readout aredetermined.

Therefore, even if image signals representing many images, in which theobject image regions shift, are fed into the neural network, theread-out conditions for the final readout can be determined efficientlyand accurately.

In the aforesaid embodiment of the ninth method in accordance with thepresent invention, the read-out conditions for the final readout aredetermined by the computer system 40. Alternatively, the imageprocessing conditions, under which the image signal SQ is to be imageprocessed, may be determined by the computer system 40.

Specifically, the method for adjusting the read-out conditions for thefinal readout by the computer system 40 provided with the neural networkmay be applied when the image processing conditions are to bedetermined. In such cases, predetermined read-out conditions may be usedwhen the final readout is carried out regardless of the characteristicsof the preliminary read-out image signal SP. On the basis of thepreliminary read-out image signal SP, the computer system 40 may adjustthe image processing conditions to be used in carrying out imageprocessing of the image signal SQ. The computer system 40 may alsoadjust both the read-out conditions and the image processing conditions.

The aforesaid embodiment of the ninth method in accordance with thepresent invention is applied to the radiation image read-out methodwherein the preliminary readout is carried out. However, the ninthmethod in accordance with the present invention is also applicable toradiation image read-out methods wherein no preliminary read-outoperations are carried out, and only the aforesaid final read-outoperations are carried out. In such cases, as an embodiment of the tenthmethod in accordance with the present invention, in the computer systemprovided with the neural network, the image processing conditions aredetermined on the basis of the image signal obtained by an appropriatemethod. At this time, the position of the center point of the pattern ofthe object is taken into consideration in the neural network.

In the embodiment of the tenth method in accordance with the presentinvention, an image stored on the stimulable phosphor sheet is read out.The tenth method in accordance with the present invention is alsoapplicable when image signals are detected from images, such as medicalimages, which have been recorded on conventional X-ray film, or thelike.

Information representing the optimum image processing conditions, whichhave thus been adjusted, is fed into an image processing unit. In theimage processing unit, image processing, such as gradation processing,is carried out on the image signal under the optimum image processingconditions.

An embodiment of the eleventh method, i.e. the method for adjustingread-out conditions and/or image processing conditions for a radiationimage, in accordance with the present invention will be describedhereinbelow. FIG. 19 is a block diagram showing the embodiment of theeleventh method in accordance with the present invention. Specifically,a probability density function 402 of an image signal 401 representing aradiation image is created. The information representing the probabilitydensity function 402 is fed into a neural network 404. Alternatively,both the information, which represents the probability density function402, and subsidiary information, which gives specifics about theradiation image, e.g. the information concerning the patient and themode in which the radiation image was recorded, are fed into the neuralnetwork 404. The neural network 404 adjusts the read-out conditions forthe final readout and/or the image processing conditions 405.

This embodiment is incorporated in the computer system 40 shown in FIG.5.

In the computer system 40, when necessary, a subdivision pattern and theshape and location of an irradiation field are determined from thepreliminary read-out image signal SP. Thereafter, the probabilitydensity function of the preliminary read-out image signal SP is created.The read-out conditions for the final readout, i.e. the sensitivity Skand the latitude Gp during the final readout, are determined by a neuralnetwork on the basis of the results of an analysis of the probabilitydensity function. By way of example, the voltage applied to aphotomultiplier 21' and the amplification factor of a logarithmicamplifier 26' are controlled in accordance with the sensitivity Sk andthe latitude Gp. Also, the image processing conditions are determined onthe basis of the results of an analysis of the probability densityfunction.

The preliminary read-out image signal SP is fed into an operation means,which carries out the embodiment of the eleventh method in accordancewith the present invention. In this embodiment, the combination of thehardware and software functions, which are incorporated in the computersystem 40 for realizing the functions of the respective means of theeleventh method in accordance with the present invention, constitutesthe examples of the respective means of the eleventh method inaccordance with the present invention.

FIG. 20 shows the probability density function of the preliminaryread-out image signal SP.

With reference to FIG. 20, the values of the preliminary read-out imagesignal SP are plotted on the horizontal axis. The relative frequency ofoccurrence of the values of the preliminary read-out image signal SP isplotted on the vertical axis at the upper part of the graph (a singleimage signal component of the preliminary read-out image signal SPcorresponding to each picture element in the X-ray image is counted asone). Also, the values of the image signal SQ obtained during the finalreadout are plotted on the vertical axis at the lower part of the graph.

The probability density function 470 of the preliminary read-out imagesignal SP is composed of a projecting part A, which corresponds to theobject image region, and a projecting part B, which corresponds to abackground region and is located on the larger image signal value sidethan the projecting part A. The values of the probability densityfunction 470 are compared to a threshold value T, starting with thesmaller value of the preliminary read-out image signal SP and workingalong the direction of increase of the image signal values. Points, atwhich the probability density function crosses through the thresholdvalue T, are found. In this manner, a first point "a" and a second point"b" are found. The values SP1 and SP2 corresponding to the points "a"and "b" are thus found. The range of the value SP1 to the value SP2 ofthe preliminary read-out image signal SP is found as corresponding tothe object image pattern in the X-ray image. The read-out conditions forthe final readout are set so that, during the final readout, the amountsof light emitted from points on the X-ray image corresponding to thevalues SP1 and SP2 of the preliminary read-out image signal SP aredetected as the minimum value SQ1 and the maximum value SQ2 of the imagesignal SQ. Specifically, the read-out conditions for the final readoutare set so that, during the final readout, the image informationrepresented by values of the emitted light signal falling within therange of SP1 to SP2 is detected as an image signal having values lyingon the straight line G1. The final readout is carried out under theread-out conditions, which have thus been set. The read-out conditionsfor the final readout are determined by the position of the straightline G1 with respect to the horizontal direction in FIG. 20 (thesensitivity Sk) and the inclination of the straight line G1 (thelatitude Gp).

However, in cases where a probability density function 470' is obtainedwhich has a projecting part A' crossing through the threshold value T,the read-out conditions for the final readout, which correspond to astraight line G1', are set by mistake. If the final readout is carriedout under the read-out conditions for the final readout, which have thusbeen set by mistake, an image signal SQ is obtained which correspondsonly to the range between the values SP1 and SP2' of the preliminaryread-out image signal SP (which range corresponds to the range A' of theprobability density function). In such cases, a new image recordingoperation must be carried out.

In the embodiment of the eleventh method in accordance with the presentinvention, such that the read-out conditions for the final readout whichcorrespond to the straight line G1 in FIG. 20 may be set even in casesdescribed above, the information representing the probability densityfunction is fed into the neural network, the learning operations of theneural network are carried out, and appropriate read-out conditions forthe final readout corresponding to the probability density function aredetermined.

In an embodiment of the twelfth method in accordance with the presentinvention, in order for the accuracy, with which the conditions areadjusted, to be kept higher, subsidiary information giving specificsabout the radiation image are fed into the neural network together withthe information representing the probability density function. Theread-out conditions for the final readout are determined in accordancewith the combination of the subsidiary information and the probabilitydensity function. In this manner, the accuracy, with which theconditions are adjusted by the neural network, can be kept high. Thesubsidiary information includes, for example, the information concerningthe patient (such as the name of the patient or the portion of theobject the image of which was recorded), and the mode in which the imagewas recorded (such as a simple image recording mode, a contrasted imagerecording mode, or a tomographic mode).

An X-ray image read-out apparatus will be described hereinbelow, inwhich a computer system wherein an embodiment of the thirteenth methodin accordance with the present invention is employed is incorporated.

FIG. 21 shows the embodiment of the thirteenth method in accordance withthe present invention. FIG. 22 shows a normalized probability densityfunction, which has been obtained by normalizing the probability densityfunction of the preliminary read-out image signal SP, and an example ofthe neural network. In this embodiment, a probability density function552 of an image signal 551 representing a radiation image is created byan operation means 559. The value of the image signal, which valuerepresents the maximum amount of the emitted light in part of theprobability density function 559 other than the part corresponding to abackground region in the radiation image, is taken as the maximum valueSmax1. The probability density function 552 is normalized in its partbetween the maximum value Smax1 and the minimum value Smin of the imagesignal 551, a normalized probability density function 554 being therebycreated. Information, which represents the normalized probabilitydensity function 554, is fed into a neural network 555 as shown in FIG.22 such that a predetermined value (the maximum value Smax1 in thiscase), which falls within the range of the maximum value Smax1 and theminimum value Smin of the image signal in the normalized probabilitydensity function 554, may always be fed into the same input unit (thelowest input unit in this case) of the neural network 555. Alternativelyboth the information, which represents the normalized probabilitydensity function 554, and subsidiary information 558, which givesspecifics about the radiation image stored on the stimulable phosphorsheet, are fed into the neural network. Information representing theread-out conditions and/or the image processing conditions is fed outfrom the neural network 555. The read-out conditions and/or the imageprocessing conditions, which are represented by the information fed outfrom the neural network 555, are corrected for the sensitivity. In thismanner, appropriate read-out conditions and/or appropriate imageprocessing conditions are set.

The embodiment of the thirteenth method in accordance with the presentinvention is incorporated in the computer system 40 shown in FIG. 5.

In the computer system 40, when necessary, a subdivision pattern and theshape and location of an irradiation field are determined from thepreliminary read-out image signal SP. Thereafter, the probabilitydensity function of the preliminary read-out image signal SP is created.The value of the image signal, which value represents the maximum amountof the emitted light in part of the probability density function otherthan the part corresponding to a background region in the radiationimage, is taken as the maximum value Smax1. The probability densityfunction is normalized in its part between the maximum value Smax1 andthe minimum value Smin of the preliminary read-out image signal SP, anormalized probability density function being thereby created.Information, which represents the normalized probability densityfunction, is fed into the neural network as shown in FIG. 22 such that,even if the values of the image signal in the normalized probabilitydensity function vary, the maximum values Smax1 and S'max1 may always befed into the same input unit (the lowest input unit in FIG. 22) of theneural network. The neural network determines the read-out conditionsfor the final readout, i.e. the sensitivity Sk' and the latitude Gp'during the final readout, on the basis of the normalized probabilitydensity function. The sensitivity Sk' and the latitude Gp' are correctedwith the maximum value Smax1. By way of example, the voltage applied toa photomultiplier 21' and the amplification factor of a logarithmicamplifier 26' are controlled in accordance with the sensitivity Sk andthe latitude Gp, which have thus been corrected.

The final readout is carried out at the final readout means 100' shownin FIG. 5 under the read-out conditions for the final readout, whichhave thus been adjusted.

The image signal SQ obtained by being digitized in the A/D converter 27'is fed into the computer system 40. In the computer system 40,appropriate image processing is carried out on the image signal SQ, andthe processed image signal is fed into a reproducing apparatus (notshown). In the reproducing apparatus, an X-ray image is reproduced fromthe image signal.

In the computer system 40, the probability density function of thepreliminary read-out image signal SP is created. The value of the imagesignal, which value represents the maximum amount of the emitted lightin part of the probability density function other than the partcorresponding to a background region in the radiation image, is taken asthe maximum value Smax1. The probability density function is normalizedin its part between the maximum value Smax1 and the minimum value Sminof the preliminary read-out image signal SP, a normalized probabilitydensity function being thereby created. Information, which representsthe normalized probability density function, is fed into the neuralnetwork as shown in FIG. 22 such that the maximum value Smax1 may alwaysbe fed into the same input unit (the lowest input unit in FIG. 22) ofthe neural network. The neural network determines the read-outconditions for the final readout and/or the image processing conditions.The image processing conditions are corrected with the maximum valueSmax1 taken as a predetermined value, which falls within the maximumvalue Smax1 and the minimum value Smin in the normalized probabilitydensity function. In this manner, the image processing conditionsappropriate for the X-ray image are adjusted.

The preliminary read-out image signal SP is fed into an operation means,which carries out the embodiment of the thirteenth method in accordancewith the present invention. In this embodiment, the combination of thehardware and software functions, which are incorporated in the computersystem 40 for realizing the functions of the respective means of thethirteenth method in accordance with the present invention, constitutesthe examples of the respective means of the thirteenth method inaccordance with the present invention.

How the probability density function is normalized in the embodiment ofthe thirteenth method in accordance with the present invention will bedescribed hereinbelow.

FIG. 23 shows the probability density functions of preliminary read-outimage signals SP. With reference to FIG. 23, the values of thepreliminary read-out image signal SP are plotted on the horizontal axis.The relative frequency of occurrence of the values of the preliminaryread-out image signal SP is plotted on the vertical axis at the upperpart of the graph (a single image signal component of the preliminaryread-out image signal SP corresponding to each picture element in theX-ray image is counted as one).

The probability density function 580 of the preliminary read-out imagesignal SP is composed of a projecting part C, which corresponds to theobject image region, and a projecting part D, which corresponds to abackground region and is located on the larger image signal value sidethan the projecting part C. The value of the preliminary read-out imagesignal, which value represents the maximum amount of the emitted lightin part of the probability density function 580 other than the partcorresponding to a background region in the radiation image, is taken asthe maximum value Smax1. Also, the minimum value of the preliminaryread-out image signal in the probability density function 580 is takenas Smin. The probability density function is normalized with its maximumvalue in its part between the maximum value Smax1 and the minimum valueSmin of the preliminary read-out image signal SP, a normalizedprobability density function being thereby created. The normalizedprobability density function corresponds only to the object image regionand is free of the image signal corresponding to the background region.

However, in cases where a probability density function 580' is obtained,which is of the preliminary read-out image signal having valuesdifferent from the probability density function 580 due to a change inthe X-ray dose, i.e. when the sensitivity of the image varies, theminimum value Smin and the maximum value Smax1 of the image signal vary.The values of the image signal in the normalized probability densityfunction fed into the neural network cover a wide range. If such anormalized probability density function is fed into the neural network,a long time will be required for the leaning operations to be carriedout. Also, appropriate read-out conditions for the final readout and/orappropriate image processing conditions cannot be obtained.

Therefore, in the embodiment of the thirteenth method in accordance withthe present invention, the information, which represents the normalizedprobability density function, is fed into the neural network such thatappropriate read-out conditions for the final readout and/or appropriateimage processing conditions can be set without being adversely affectedby the sensitivity. Specifically, as shown in FIG. 22, the information,which represents the normalized probability density function, is fedinto the neural network such that a predetermined value (the maximumvalue Smax1 in FIGS. 22 and 23), which falls within the range of themaximum value Smax1 and the minimum value Smin of the image signal inthe normalized probability density function, may always be fed into thesame input unit of the neural network. In this manner, approximately thesame conditions are determined even if the image signal values in thenormalized probability density function fed into the neural networkvary. The read-out conditions for the final readout and/or the imageprocessing conditions, which are fed out from the neural network, arecorrected in accordance with the predetermined value (the maximum valueSmax1 in FIGS. 22 and 23), and the read-out conditions for the finalreadout and/or the image processing conditions appropriate for the imageare thereby adjusted.

In the aforesaid embodiment, the maximum value Smax1 is employed as thepredetermined value, which falls within the range of the maximum valueSmax1 and the minimum value Smin of the image signal in the normalizedprobability density function. Alternatively, any of other values may beemployed which falls within the range of the maximum value and theminimum value of the image signal in the normalized probability densityfunction. For example, the minimum value of the image signal, anintermediate value between the maximum value and the minimum value, orthe like, may be employed. In cases where the minimum value is employedas the predetermined value, it is fed into the top input unit in FIG.22. In cases where the intermediate value between the maximum value andthe minimum value is employed as the predetermined value, it is fed intoan intermediate input unit in FIG. 22.

As described above, the predetermined value (the maximum value Smax1 inthis case) of the image signal in the normalized probability densityfunction is always fed into the same input unit of the neural network.In such case, other values of the image signal are sequentially fed intothe input units adjacent to the input unit determined with reference tothe predetermined value. For example, in cases where the maximum valueSmax1 is fed into the lowest input unit, the values of the image signalfrom the maximum value Smax1 to the minimum value Smin in the normalizedprobability density function are fed sequentially into input units inthe order from the lowest input unit to the top input unit. In caseswhere the minimum value is employed as the predetermined value, thevalues of the image signal from the minimum value Smin to the maximumvalue Smax1 in the normalized probability density function are fedsequentially into input units in the order from the top input unit tothe lowest input unit. In cases where an intermediate value between themaximum value and the minimum value is employed as the predeterminedvalue, the values of the image signal from the minimum value Smin to themaximum value Smax1 in the normalized probability density function arefed sequentially into input units in the order from an intermediateinput unit to the top and lowest input units.

In an embodiment of the fourteenth method in accordance with the presentinvention, in order for the accuracy, with which the conditions areadjusted, to be kept higher, subsidiary information giving specificsabout the radiation image are fed into the neural network together withthe information representing the normalized probability densityfunction. The read-out conditions for the final readout are determinedin accordance with the combination of the subsidiary information and thenormalized probability density function. In this manner, the accuracy,with which the conditions are adjusted by the neural network, can bekept high.

How the learning operations of the neural network are repeated andappropriate read-out conditions for the final readout are adjustedthereby will be described hereinbelow. The neural network is constitutedas shown in FIG. 3. Signals F1, F2, . . . , Fn1 fed into the first layer(the input layer) are the signals representing the probability densityfunction or the normalized probability density function of the imagesignal components of the preliminary read-out image signal SPrepresenting the picture elements in the X-ray image. Two outputs y₁ ³and y₂ ³ obtained from the third layer (the output layer) are thesignals corresponding to the sensitivity and the latitude during thefinal readout.

Preliminary read-out image signals are obtained in the manner describedabove from a plurality of stimulable phosphor sheets storing X-rayimages, for which the appropriate read-out conditions for the finalreadout are known. The preliminary read-out image signal is then thinnedout. In this manner, the n1 number of inputs F1, F2, . . . , Fn1 areobtained. The n1 number of inputs F1, F2, . . . , Fn1 are fed into theneural network shown in FIG. 3, and the learning operations of theneural network are repeated in the same manner as that described above.The instructor signals representing the read-out conditions for thefinal readout appropriate for the image represent the sensitivity y₁ ³and the latitude y₂ ³. At the time at which the learning operations arecompleted, the two outputs y₁ ³ and y₂ ³ represents the appropriatesensitivity and the appropriate latitude during the final readout.

Therefore, after the learning operations are completed, a probabilitydensity function or a normalized probability density function is createdfrom a preliminary read-out image signal SP representing an X-ray image,and is fed into the neural network shown in FIG. 3. The outputs y₁ ³ andy₂ ³ obtained from the neural network are utilized as signalsrepresenting the read-out conditions (i.e. the sensitivity and thelatitude) for the final readout appropriate for the X-ray image. Becausethe learning operations have been carried out in the manner describedabove, the signals accurately represent the appropriate read-outconditions for the final readout.

How the sensitivity is corrected in the embodiment of the thirteenthmethod in accordance with the present invention will be describedhereinbelow. In this embodiment, as shown in FIG. 22, the normalizedprobability density function is fed into the neural network such thatthe maximum value Smax1 may always be fed into the same input unit. Theread-out conditions for the final readout are determined which do notdepend on the sensitivity even if the sensitivity of the image signalvalues in the normalized probability density function changes.Therefore, the read-out conditions (i.e. the sensitivity and thelatitude) for the final readout, which are determined by the neuralnetwork, are fed out as information concerning the relative position,instead of being fed out as absolute values. As described above, thelatitude corresponds to the ratio of the largest amount of emittedlight, which is converted into the image signal, to the smallest amountof emitted light, which is converted into the image signal.Specifically, the latitude corresponds to the ratio of the maximum valueSmax1 to the minimum value Smin. From the neural network, informationrepresenting the ratio of the maximum value Smax1 to the minimum valueSmin is fed out as the latitude. Therefore, the latitude need not becorrected. In this embodiment, the sensitivity is obtained from theneural network as the information concerning the position between theminimum value Smin and the maximum value Smax1, i.e. as the percentageof the position of sensitivity of the necessary region (the hatchedregion in FIG. 22) from the point corresponding to the minimum valueSmin toward the point corresponding to the maximum value Smax1 of thepreliminary read-out image signal SP in the normalized probabilitydensity function. Because the minimum value Smin and the maximum valueSmax1 are already known, the sensitivity is corrected with the formula

    Sensitivity Sk=Smax1-(Smax1-Smin)×Sk1 (%)            (24)

The final read-out conditions for the final readout (the sensitivity andthe latitude) are thereafter adjusted.

In the manner described above, the read-out conditions for the finalreadout are determined by the neural network and the sensitivitycorrecting means. The voltage applied to the photomultiplier 27', theamplification factor of the amplifier 26' of the final read-out means100', or the like, is controlled in accordance with the read-outconditions for the final readout. The final readout is carried out underthe controlled conditions.

In the aforesaid embodiments of the eleventh and thirteenth methods inaccordance with the present invention, the read-out conditions for thefinal readout are adjusted by the computer system 40. Alternatively,predetermined read-out conditions may be used when the final readout iscarried out regardless of the characteristics of the preliminaryread-out image signal SP. On the basis of the preliminary read-out imagesignal SP, the computer system 40 may adjust the image processingconditions to be used in carrying out image processing of the imagesignal SQ. The computer system 40 may also adjust both the read-outconditions and the image processing conditions.

The aforesaid embodiments of the eleventh and thirteenth methods inaccordance with the present invention are applied to the radiation imageread-out apparatus wherein the preliminary readout is carried out.However, the eleventh and thirteenth methods in accordance with thepresent invention are also applicable to radiation image read-outapparatuses wherein no preliminary read-out operations are carried out,and only the aforesaid final read-out operations are carried out. Inthese cases, an image signal is obtained by use of predeterminedread-out conditions. Based on the image signal, image processingconditions are calculated by the computer system 40. The image signal isprocessed under the calculated image processing conditions.

An embodiment of the nineteenth apparatus, i.e. the radiation imageanalyzing apparatus, in accordance with the present invention will bedescribed hereinbelow. This embodiment is incorporated in the computersystem 40 shown in FIG. 5.

In the computer system 40, the shape and location of an irradiationfield are determined from the preliminary read-out image signal SP.Thereafter, the read-out conditions for the final readout, i.e. thesensitivity and the contrast during the final readout, are determined.By way of example, the voltage applied to the photomultiplier 27', theamplification factor of the amplifier 26' of the final read-out means100', or the like, is controlled in accordance with the read-outconditions for the final readout. In this embodiment, the read-outconditions for the final readout constitute the characteristic measuresin the nineteenth apparatus in accordance with the present invention.

How the computer system 40 determines the shape and location of theirradiation field from the preliminary read-out image signal SP andadjusts the read-out conditions for the final readout will be describedhereinbelow.

FIG. 24 is an explanatory view showing an example of an X-ray image, apreliminary read-out image signal SP representing the X-ray image, anddifferentiated values ΔSP of the preliminary read-out image signal, theexplanatory view serving as an aid in explaining an embodiment of thedetermination means in the nineteenth apparatus in accordance with thepresent invention.

With reference to FIG. 24, an image of an object 603 (the head of ahuman body in this case) is stored in the region inside of anirradiation field 602 on the stimulable phosphor sheet 11. In thisembodiment, the center point C of the stimulable phosphor sheet 11 isselected as the predetermined point located in the region inside of theirradiation field 602. Differentiation operations are carried out on theimage signal components of the preliminary read-out image signal SPcorresponding to the picture elements arrayed along each of a pluralityof lines 605, 605, . . . which extend radially from the center point C.The point for which the corresponding value of the preliminary read-outimage signal SP decreases sharply is detected as a contour point, whichis located on the contour of the irradiation field.

How a contour point is detected along the ξ axis, which is one of thelines 605, 605, . . . will be described hereinbelow.

Curve A represents the values of the image signal components of thepreliminary read-out image signal SP corresponding to the pictureelements arrayed along the ξ axis.

The values of the image signal components of the preliminary read-outimage signal SP are largest for a background region 606 which is locatedoutside of the region defined by the object image 603 but inside of theirradiation field 602 and upon which X-rays impinged directly. Thevalues of the image signal components of the preliminary read-out imagesignal SP corresponding to the contour of the irradiation field 602decreases sharply.

Curve B represents the results of differentiation carried out on theimage signal components of the preliminary read-out image signal SPrepresented by curve A starting from that signal component correspondingto the center point C and continuing with components corresponding topositions lying in the negative direction along the ξ axis (i.e.leftward in FIG. 24) and in the positive direction along the ξ axis(i.e. rightward in FIG. 24).

Curve B has a single major peak a1 which projects downwardly for theline extending from the center point C in the negative direction alongthe ξ axis. Therefore, the position on the stimulable phosphor sheet 11which corresponds to the peak a1 is detected as a contour point on theline extending from the center point C in the negative direction alongthe ξ axis.

On the line extending from the center point C in the positive directionalong the ξ axis, curve B has a peak a2 which projects downwardly.Therefore, the position corresponding to the peak a2 is detected as acontour point on the line extending from the center point C in thepositive direction along the ξ axis.

In the manner described above, contour points 607, 607, . . . aredetected respectively on a plurality of the lines 605, 605, . . . eachof which connects the center point C with the edge of the stimulablephosphor sheet 11. After the contour points 607, 607, . . . aredetected, lines connecting them may be assumed to follow the contour ofthe irradiation field. One of several methods is used to find the linesconnecting the contour points 607, 607, . . . , for example, a methodwherein prospective contour points remaining after a smoothing processhas been carried out are connected together, a method wherein the methodof least squares is applied to find a plurality of straight lines andwherein the straight lines are then connected together, or a methodwherein a spline curve or the like is applied. In this embodiment, aplurality of straight lines connecting the contour points are found byutilizing a Hough transformation. The processing done to find thestraight lines will hereinbelow be described in detail.

A corner (the lower corner of the left edge) of the stimulable phosphorsheet 11 shown in FIG. 24 is taken as the origin, and the x and y axesare set as shown in FIG. 24. The coordinates of the contour points areexpressed as (x1,y1), (x2,y2), . . . , (xn,yn). These coordinates arerepresented by (xo,yo). As shown in FIG. 25, calculations are made tofind the curves expressed as

    ρ=xo cos θ+yo sin θ                        (25)

where xo and yo are fixed numbers, for each contour point coordinate(xo,yo). FIG. 25 shows the curves thus obtained, and the number ofcurves equals the number of the contour point coordinates (xo,yo).

Then, calculations are made to find the coordinates (ρo,θo) of thepoints where the curves intersect and where the number of curvesintersecting at each point (ρo,θo) is not smaller than a predeterminednumber Q. Because of errors in finding the contour point coordinates(xo,yo), many curves rarely intersect exactly at a single point.Therefore, by way of example, in the case where multiple sets of twocurves have intersections spaced from one another by only smalldistances not longer than a predetermined distance, the point ofintersection at the middle of the group of the intersections is taken asthe aforesaid intersection (ρo,θo). Then, for each point of intersection(ρo,θo), a straight line is calculated, which is expressed as

    ρo=x cos θo+y sin θo                       (26)

on the x-y orthogonal coordinate system. A plurality of the contourpoint coordinates (xo,yo) lie along the straight line thus calculated.In cases where the contour points 607, 607, . . . are distributed asshown in FIG. 24, the straight lines L1, L2, L3, L4 and L5 shown in FIG.26 are obtained. They are extensions of the lines forming the contour ofthe irradiation field 602 shown in FIG. 24. The region surrounded by theplurality of straight lines L1, L2, L3, . . . , Ln obtained in thismanner is then detected, and said region is detected as the irradiationfield 602. Specifically, for example, the shape of the region is foundin the manner described below. The computer system 40 stores thecoordinates for line segments M1, M2, M3, . . . , Mm connecting thecorners of the stimulable phosphor sheet 11 with the center point C(four line segments in cases where the stimulable phosphor sheet 11 isrectangular), and detects whether or not each of the line segments M1 toMm intersects with each of the straight lines L1 to Ln. In cases wherean intersection is present, the computer system 40 divides thestimulable phosphor sheet 11 into two regions: one including the cornerof the stimulable phosphor sheet 11 to which the line segment isconnected and delineated by the straight line and the other includingthe remainder of the stimulable phosphor sheet 11. The computer system40 then discards the region including the corner. This operation iscarried out for all of the straight lines L1 to Ln and the line segmentsM1 to Mm, and the region surrounded by the straight lines L1 to Ln isnot discarded. The region thus obtained is detected as the irradiationfield 602 shown in FIG. 24.

After the shape and location of the irradiation field 602 is found, thepreliminary read-out image signal SP corresponding to the region insideof the irradiation field is fed into the neural network. The read-outconditions for the final readout are adjusted so that, during the finalreadout, the image signal corresponding to the region inside of theirradiation field may be detected under appropriate read-out conditions.

FIG. 27 shows some of the picture elements located in the region insideof the irradiation field in an X-ray image. Each square cell representsa single picture element.

In this embodiment, after the shape and location of the irradiationfield 602 have been determined, only the image signal components of thepreliminary read-out image signal SP, which represent the pictureelements hatched in FIG. 27, are sampled from those corresponding to thepicture elements located in the region inside of the irradiation field602. The sampled image signal components are fed into the neuralnetwork. The preliminary read-out image signal SP corresponding to theregion inside of the irradiation field 602 need not necessarily bethinned out. However, when the preliminary read-out image signal SPcorresponding to the region inside of the irradiation field 602 isthinned out and fed into the neural network, the number of input pointsof the neural network can be reduced even further.

In most images, the major part of the image is present in the vicinityof the center part in the region inside of the irradiation field.Therefore, the preliminary read-out image signal SP corresponding to theregion inside of the irradiation field 602 may be thinned out such thatmore image signal components remain, which correspond to the center partin the region inside of the irradiation field 602, and less componentsremain, which correspond to the peripheral areas.

The neural network is constituted as shown in FIG. 3. Signals F1, F2, .. . , Fn1 fed into the first layer (the input layer) are the imagesignal components of the preliminary read-out image signal SPrepresenting the picture elements located in the region inside of theirradiation field 602 in the X-ray image. The preliminary read-out imagesignal has been thinned out in the manner described above with referenceto FIG. 27. Two outputs y₁ ³ and y₂ ³ obtained from the third layer (theoutput layer) are the signals corresponding to the sensitivity and thecontrast during the final readout.

Preliminary read-out image signals are obtained in the manner describedabove from a plurality of stimulable phosphor sheets storing X-rayimages, for which the appropriate read-out conditions for the finalreadout are known. The preliminary read-out image signal SP is thenthinned out in the manner shown in FIG. 27. In this manner, the n1number of inputs F1, F2, . . . , Fn1 are obtained. The n1 number ofinputs F1, F2, . . . , Fn1 are fed into the neural network shown in FIG.3, and the learning operations of the neural network are repeated in thesame manner as that described above. The instructor signals representingthe read-out conditions for the final readout appropriate for the imagerepresent the sensitivity y₁ ³ and the contrast y₂ ³. At the time atwhich the learning operations are completed, the two outputs y₁ ³ and y₂³ represents the appropriate sensitivity and the appropriate contrastduring the final readout.

Therefore, after the learning operations are completed, a preliminaryread-out image signal SP representing an X-ray image, for which theappropriate read-out conditions for the final readout are unknown, isobtained. The shape and location of the irradiation field in the X-rayimage are then determined from the preliminary read-out image signal SP.The preliminary read-out image signal SP corresponding to the regioninside of the irradiation field is fed into the neural network shown inFIG. 3. The outputs y₁ ³ and y₂ ³ obtained from the neural network areutilized as signals representing the read-out conditions (i.e. thesensitivity and the contrast) for the final readout appropriate for theX-ray image. Because the learning operations have been carried out inthe manner described above, the signals accurately represent theappropriate read-out conditions for the final readout.

In the aforesaid embodiment of the nineteenth apparatus in accordancewith the present invention, the computer system determines the read-outconditions for the final readout. Alternatively, predetermined read-outconditions may be used when the final readout is carried out regardlessof the characteristics of the preliminary read-out image signal SP. Onthe basis of the preliminary read-out image signal SP, the computersystem 40 may adjust the image processing conditions to be used incarrying out image processing of the image signal SQ. The computersystem 40 may also adjust both the read-out conditions and the imageprocessing conditions.

The aforesaid embodiment of the nineteenth apparatus in accordance withthe present invention is applied to the radiation image read-outapparatus wherein the preliminary readout is carried out. However, thenineteenth apparatus in accordance with the present invention is alsoapplicable to radiation image read-out apparatuses wherein nopreliminary read-out operations are carried out, and only the aforesaidfinal read-out operations are carried out. In such cases, the imageread-out operation is carried out under predetermined read-outconditions, and an image signal is thereby obtained. In the computersystem, the image processing conditions are determined on the basis ofthe image signal. The image signal is image processed under the imageprocessing conditions thus determined.

The nineteenth apparatus in accordance with the present invention isalso applicable when conventional X-ray film, or the like, is used.

Also, the nineteenth apparatus in accordance with the present inventionis not limited to the determination of the read-out conditions for thefinal readout and/or the image processing conditions. For example, theportion of the object the image of which was recorded, or the like, maybe employed as the characteristic measures.

An embodiment of the twentieth apparatus, i.e. the radiation imageanalyzing apparatus, in accordance with the present invention will bedescribed hereinbelow.

This embodiment is incorporated in the computer system 40 shown in FIG.5.

In the computer system 40, the shape and location of an irradiationfield are determined from the preliminary read-out image signal SP.Thereafter, the read-out conditions for the final readout, i.e. thesensitivity and the contrast during the final readout, are determined.By way of example, the voltage applied to the photomultiplier 27', theamplification factor of the amplifier 26' of the final read-out means100', or the like, is controlled in accordance with the read-outconditions for the final readout.

In the computer system 40, by using the neural network, the subdivisionpattern of radiation images, the shape and location of an irradiationfield, the orientation in which the object was placed when the image ofthe object was recorded, and/or the portion of the object the image ofwhich was recorded are determined from the preliminary read-out imagesignal SP. Thereafter, the read-out conditions for the final readout aredetermined.

As shown in FIG. 28, the preliminary read-out image signal SP is fedinto a neural network 745. The neural network 745 determines thesubdivision pattern of radiation images, the shape and location of anirradiation field, the orientation in which the object was placed whenthe image of the object was recorded, and/or the portion of the objectthe image of which was recorded are determined from the preliminaryread-out image signal SP. The results of the determination are fed outas the characteristic measures from the neural network 745.

As shown in FIG. 27, only the image signal components of the preliminaryread-out image signal SP, which represent the picture elements hatchedin FIG. 27, may be sampled. Only the sampled image signal components maybe fed into the neural network 745. In this manner, the number of inputpoints of the neural network can be reduced. In most images, the majorpart of the image is present in the vicinity of the center part in theimage. Therefore, the preliminary read-out image signal SP may bethinned out such that more image signal components remain, whichcorrespond to the center part in the image, and less components remain,which correspond to the peripheral areas.

The neural network is constituted as shown in FIG. 3. Signals F1, F2, .. . , Fn1 fed into the first layer (the input layer) are the imagesignal components of the preliminary read-out image signal SP. Thepreliminary read-out image signal has been thinned out in the mannerdescribed above with reference to FIG. 27 Two outputs y₁ ³ and y₂ ³obtained from the third layer (the output layer) are the signalsrepresenting the shape and location of the irradiation field (a circularirradiation field, a rectangular irradiation field, or the like) and theportion of the object the image of which was recorded (the head, thechest, the shoulder, the arm, or the like).

Preliminary read-out image signals are obtained in the manner describedabove from a plurality of stimulable phosphor sheets storing X-rayimages, for which the shape and location of the irradiation field andthe portion of the object, the image of which was recorded, are known.The preliminary read-out image signal SP is then thinned out in themanner shown in FIG. 27. In this manner, the n1 number of inputs F1, F2,. . . , Fn1 are obtained. The n1 number of inputs F1, F2, . . . , Fn1are fed into the neural network shown in FIG. 3, and the learningoperations of the neural network are repeated in the same manner as thatdescribed above. The instructor signals representing the results ofdetermination, which are correct for the image, represent thex-coordinate y₁ ³ and the y-coordinate y₂ ³. At the time at which thelearning operations are completed, the two outputs y₁ ³ and y₂ ³accurately represents the shape and location of the irradiation fieldand the portion of the object the image of which was recorded.

In cases where the signal representing the irradiation field represents,for example, only whether the irradiation field is circular orrectangular, one of the two outputs may represent 1 (for the circularirradiation field) or 0 (for the rectangular irradiation field). In suchcases, the determination can be carried out very easily by the neuralnetwork.

Also, for the portion of the object the image of which was recorded,several portions of the object may be represented by numerals. In thismanner, the determination can be carried out very easily by the neuralnetwork.

After the learning operations are completed, a preliminary read-outimage signal SP representing an X-ray image, for which the shape andlocation of the irradiation field and the portion of the object, theimage of which was recorded, are unknown, is obtained. The preliminaryread-out image signal SP is fed into the neural network shown in FIG. 3.The outputs y₁ ³ and y₂ ³ obtained from the neural network are utilizedas signals representing the shape and location of the irradiation fieldand the portion of the object, the image of which was recorded. Becausethe learning operations have been carried out in the manner describedabove, the signals accurately represent the shape and location of theirradiation field and the portion of the object, the image of which wasrecorded.

By way of example, the voltage applied to the photomultiplier 27', theamplification factor of the amplifier 26' of the final read-out means100', or the like, is controlled in accordance with the signalsaccurately representing the shape and location of the irradiation fieldand the portion of the object, the image of which was recorded, thesignals being obtained from the neural network. The final readout iscarried out under the controlled conditions.

In the aforesaid embodiment of the twenties apparatus in accordance withthe present invention, the shape and location of the irradiation fieldand the portion of the object, the image of which was recorded, aredetermined by the neural network. The subdivision pattern and theorientation, in which the object was placed when the image of the objectwas recorded, can also be determined in the same manner by the neuralnetwork. In such cases, as the two outputs, the signals representing thesubdivision pattern and the orientation, in which the object was placedwhen the image of the object was recorded, are generated.

In cases where the signal representing the orientation, in which theobject was placed when the image of the object was recorded, represents1 (frontal orientation) or 0 (side orientation), the determination canbe carried out very easily by the neural network.

Also, the signal representing the subdivision pattern may representsnumerals assigned to four patterns (e.g., a two-on-one subdivisionpattern having two radiation images which are vertically adjacent toeach other, a two-on-one subdivision pattern having two radiation imageswhich are horizontally adjacent to each other, a four-on-one subdivisionpattern having four radiation images which are vertically andhorizontally adjacent to each other, and a one-on-one pattern). In thismanner, the determination can be carried out very easily by the neuralnetwork.

How a binary pattern signal representing the shape and location of theirradiation field, which have been determined by the neural network, ispost-processed will be described hereinbelow.

The signal y₁ ³ fed out of the neural network represents the shape andlocation of the irradiation field. However, the signal is slightlyinaccurate for the edge of the irradiation field. Therefore, the shapeof the irradiation field, which is formed on the basis of the binarypattern signal, is not completely straight with respect to the edge ofthe irradiation field.

Therefore, a binary image process should preferably be carried out onthe binary pattern signal obtained from the neural network. With such aprocess, a signal accurately representing the edge of the irradiationfield can be obtained.

First, how a figure fusing process (a binary image smoothing process),which is an example of the binary image process, is carried out will bedescribed below. In general, the figure fusing process is composed oftwo basic processes: i.e. contraction and dilatation.

The contraction process is also referred to as the erosion process. Withthis process, all of picture elements located at the boundary of afigure are eliminated (i.e., the picture elements, which have been setto 1, are converted into the picture elements having been set to 0).Specifically, a picture element in an input image fij is converted intoa picture element in an output image gij, the conversion being expressedas ##EQU16##

The dilatation process is also referred to as the expansion process orthe propagation process. With the dilatation process, the pictureelements located at the boundary of a figure are increased (i.e., thepicture elements, which have been set to 0, are converted into thepicture elements having been set to 1). Specifically, a picture elementin an input image fij is converted into a picture element in an outputimage gij, the conversion being expressed as ##EQU17##

When the basic contraction and dilatation processes are combined, mostof the noise components in a binary image can be eliminated. Therefore,the binary image can be smoothed.

For example, as shown in FIG. 29A, in cases where an isolated point "a"having been set to 1 is present in the original image, in order for theisolated point "a" to be eliminated, the contraction process is carriedout on the original image, and it is thus converted into the image shownin FIG. 29B. Thereafter, the dilatation process is carried out toconvert the image of FIG. 29B into the image shown in FIG. 29C. In FIGS.29A, 29B, 29C, 30A, 30B, and 30C, circles indicate the picture elementshaving been set to 1, and dots indicate the picture elements having beenset to 0.

In the image which has been obtained as shown in FIG. 29C, no isolatedpoint "a" is present. In this manner, the isolated point "a" can beeliminated.

As shown in FIG. 30A, in cases where a missing point "b" (i.e. anisolated point having been set to 0) is present in the original image,in order for the missing point "b" to be eliminated, the dilatationprocess is carried out on the original image, and it is thus convertedinto the image shown in FIG. 30B. Thereafter, the contraction process iscarried out to convert the image of FIG. 30B into the image shown inFIG. 30C. In the image which has been obtained as shown in FIG. 30C, nomissing point "b is present. In this manner, the missing point "b" canbe eliminated.

FIG. 31 shows an example of how the figure fusing process is applied tothe binary pattern signal representing the shape and location of theirradiation field.

With reference to FIG. 31, a contraction process 701 and a dilatationprocess 702 are carried out in this order on the binary pattern signal,which has been obtained from the neural network and represents the shapeand location of the irradiation field. Thereafter, a dilatation process703 and a contraction process 704 are carried out in this order.

By carrying out the contraction process 701 and then carrying out thedilatation process 702 on the binary pattern signal representing theshape and location of the irradiation field, isolated points present inthe vicinity of the edge of the irradiation field can be eliminated. Bycarrying out the dilatation process 703 and then carrying out thecontraction process 704 on the binary pattern signal representing theshape and location of the irradiation field, missing points present inthe vicinity of the edge of the irradiation field can be eliminated.

Therefore, when the isolated point eliminating processes and the missingpoint eliminating processes are carried out sequentially on the binarypattern signal representing the shape and location of the irradiationfield, the boundary in the binary pattern can be smoothed. As a result,a binary pattern signal, which accurately represents the shape andlocation of the irradiation field at its edge, can be obtained.

A different example of the binary image process will be describedhereinbelow.

With reference to FIG. 32, in this example, the edge of the irradiationfield is accurately detected by carrying out differentiation processingon the signal components of the binary pattern signal corresponding tothe edge of the irradiation field.

Specifically, with the binary pattern signal, which has been obtainedfrom the neural network and represents the shape and location of theirradiation field, the boundary in the binary pattern does notnecessarily coincide with the edge of the irradiation field. However,the amount of such an error is small. Therefore, by carrying outdifferentiation processing only on the signal components of the binarypattern signal corresponding to the boundary in the binary pattern, theedge of the irradiation field can be detected and accurately.

On the basis of the shape and location of the irradiation field, whichhave been determined by the aforesaid two binary image processes, theread-out conditions for the final readout and/or the image processingconditions are determined.

How the signal, which is obtained from the neural network and representsthe portion of the object the image of which was recorded, ispost-processed will be described hereinbelow.

As described above, the read-out conditions for the final readout and/orthe image processing conditions are determined on the basis of theinformation, which is obtained from the neural network and representsthe portion of the object the image of which was recorded. In caseswhere a pattern of an artificial bone or a plaster cast is included inthe image, the ordinary image analysis cannot be carried out fordetermining the conditions.

In such cases, the neural network judges whether a pattern of anartificial bone or a plaster cast is or is not included in the image.

The judgment is made based on the signal y₂ ³, which is obtained fromthe neural network and represents the portion of the object the image ofwhich was recorded.

In cases where, from the signal y₂ ³ representing the portion of theobject the image of which was recorded, it has been judged that apattern of an artificial bone is present in the image, the ordinaryimage analysis is not carried out, but a special analysis for a patternof an artificial bone is carried out. For example, as disclosed inJapanese Unexamined Patent Publication No. 61(1986)-170729, apredetermined cumulative correction value is added to the ordinary imageanalysis. In cases where it has been judged that a pattern of a plastercast is included in the image, the ordinary image analysis is notcarried out, but a fixed sensitivity read-out operation is carried out.Alternatively, an alarm is issued, and an image analysis (an interactiveprocessing as disclosed in Japanese Unexamined Patent Publication No.61(1986)-156250) is carried out manually.

The ordinary image analysis is carried out in cases where, from thesignal y₂ ³ representing the portion of the object the image of whichwas recorded, it has been judged that no pattern of an artificial boneor a plaster cast is included in the image.

In the aforesaid embodiment of the twentieth apparatus in accordancewith the present invention, the computer system determines the read-outconditions for the final readout. Alternatively, predetermined read-outconditions may be used when the final readout is carried out regardlessof the characteristics of the preliminary read-out image signal SP. Onthe basis of the preliminary read-out image signal SP, the computersystem 40 may adjust the image processing conditions to be used incarrying out image processing of the image signal SQ. The computersystem 40 may also adjust both the read-out conditions and the imageprocessing conditions.

The aforesaid embodiment of the twentieth apparatus in accordance withthe present invention is applied to the radiation image read-outapparatus wherein the preliminary readout is carried out. However, thetwentieth apparatus in accordance with the present invention is alsoapplicable to radiation image read-out apparatuses wherein nopreliminary read-out operations are carried out, and only the aforesaidfinal read-out operations are carried out. In such cases, the imageread-out operation is carried out under predetermined read-outconditions, and an image signal is thereby obtained. In the computersystem, the image processing conditions are determined on the basis ofthe image signal. The image signal is image processed under the imageprocessing conditions thus determined.

The twentieth apparatus in accordance with the present invention is alsoapplicable when conventional X-ray film, or the like, is used.

We claim:
 1. An apparatus for adjusting read-out conditions and/or imageprocessing conditions for a radiation image, wherein a first imagesignal representing a radiation image of an object is obtained byexposing a stimulable phosphor sheet, on which the radiation image hasbeen stored, to stimulating rays, which cause the stimulable phosphorsheet to emit light in proportion to the amount of energy stored thereonduring its exposure to radiation, the emitted light being detected,asecond image signal representing the radiation image is thereafterobtained by again exposing the stimulable phosphor sheet to stimulatingrays, the light emitted by the stimulable phosphor sheet being detected,and read-out conditions, under which the second image signal is to beobtained, and/or image processing conditions, under which the secondimage signal having been obtained is to be image processed, are adjustedon the basis of the first image signal, the apparatus for adjustingread-out conditions and/or image processing conditions for a radiationimage comprising:i) a storage means for storing information representinga standard pattern of radiation images, ii) a signal transforming meansfor transforming said first image signal representing said radiationimage into a transformed image signal representing the radiation image,which has been transformed into said standard pattern, and iii) acondition adjusting means provided with a neural network, which receivessaid transformed image signal and feeds out information representing theread-out conditions and/or the image processing conditions.
 2. Anapparatus for adjusting read-out conditions and/or image processingconditions for a radiation image as defined in claim 1 wherein saidtransformation of said first image signal representing said radiationimage into a transformed image signal representing the radiation image,which has been transformed into said standard pattern, is one ofreversal, rotation, position adjustment, enlargement, and reduction ofsaid radiation image represented by said first image signal, or acombination of two or more of these processes.
 3. An apparatus foradjusting image processing conditions for a radiation image, whereinimage processing conditions, under which an image signal is to be imageprocessed, are adjusted on the basis of the image signal representing aradiation image of an object,the apparatus for adjusting imageprocessing conditions for a radiation image comprising:i) a storagemeans for storing information representing a standard pattern ofradiation images, ii) a signal transforming means for transforming saidimage signal representing said radiation image into a transformed imagesignal representing the radiation image, which has been transformed intosaid standard pattern, and iii) a condition adjusting means providedwith a neural network, which receives said transformed image signal andfeeds out information representing the image processing conditions. 4.An apparatus for adjusting read-out conditions and/or image processingconditions for a radiation image as defined in claim 3 wherein saidtransformation of said image signal representing said radiation imageinto a transformed image signal representing the radiation image, whichhas been transformed into said standard pattern, is one of reversal,rotation, position adjustment, enlargement, and reduction of saidradiation image represented by said image signal, or a combination oftwo or more of these processes.
 5. A method for adjusting read-outconditions and/or image processing conditions for a radiation image,wherein a first image signal representing a radiation image of an objectis obtained by exposing a stimulable phosphor sheet, on which theradiation image has been stored, to stimulating rays, which cause thestimulable phosphor sheet to emit light in proportion to the amount ofenergy stored thereon during its exposure to radiation, the emittedlight being detected,a second image signal representing the radiationimage is thereafter obtained by again exposing the stimulable phosphorsheet to stimulating rays, the light emitted by the stimulable phosphorsheet being detected, and read-out conditions, under which the secondimage signal is to be obtained, and/or image processing conditions,under which the second image signal having been obtained is to be imageprocessed, are adjusted on the basis of the first image signal, themethod for adjusting read-out conditions and/or image processingconditions for a radiation image comprising the steps of:i) carrying outa condition adjustment by using a neural network, which receives saidfirst image signal and feeds out information representing the read-outconditions and/or the image processing conditions, and ii) when learningof said neural network is carried out such that information representingappropriate read-out conditions and/or appropriate image processingconditions may be fed out, utilizing an image signal representing aradiation image, in which a pattern of a predetermined specific regionof interest of the radiation image is embedded, and read-out conditionsand/or image processing conditions, which have been determined as beingoptimum for the pattern of said predetermined specific region ofinterest.
 6. A method for adjusting image processing conditions for aradiation image, wherein image processing conditions, under which animage signal is to be image processed, are adjusted on the basis of theimage signal representing a radiation image of an object,the method foradjusting image processing conditions for a radiation image comprisingthe steps of:i) carrying out a condition adjustment by using a neuralnetwork, which receives said image signal and feeds out informationrepresenting the image processing conditions, and ii) when learning ofsaid neural network is carried out such that information representingappropriate image processing conditions may be fed out, utilizing animage signal representing a radiation image, in which a pattern of apredetermined specific region of interest of the radiation image isembedded, and image processing conditions, which have been predeterminedas being optimum for the pattern of said predetermined specific regionof interest.